Understanding how satisfaction with individual trip legs aggregates to the overall travel experience for different types of trips will enable the identification of the trip legs that are most impactful. For this purpose we analyze data on retrospective evaluations of entire multi-modal trip experiences and satisfaction with individual trip legs. We formulate and describe alternative aggregation rules and underpin them in theory and previous empirical findings. The results of a series of regression models show that for a large number of multi-modal trip configurations normative rules can better reproduce overall travel satisfaction than heuristic rules. This indicates that all trip legs need to be considered when evaluating the overall travel experience, especially for trips legs involving waiting and/or transferring time. In particular, weighting satisfaction with individual trip legs with perceived trip leg durations yielded the best predictor of overall travel satisfaction. No evidence for a disproportional effect of the last or most exceptional part of the trip was found. This research contributes to the literature on combining multi-episodic experiences and provides novel empirical evidence in the transport domain. 

Classifying public transport service attributes based on their influence on overall traveler satisfaction can assist stakeholders and practitioners in introducing cost-efficient measures. To date most studies employed methods that were based on the assumption that the impact of service attributes on traveler satisfaction is entirely linear and symmetric. This study examines whether service attributes have a non-linear and asymmetric influence on the overall travel experience by employing the Three-factor theory (basic, performance and exciting factors). The analysis is conducted for different traveler segments depending on their level of captivity, travel frequency by public transport and travel mode used, and is based on a relatively large sample size collected for Stockholm County. Moreover, the estimated models control for important socio-demographic and travel characteristics that have been insofar overlooked. Results are presented in the form of a series of multi-level cubes that represent different essentiality of traveler needs which provide a useful methodological framework to further design quality service improvements that can be applied to various geographical contexts. Our findings highlight that a “one size fits all” approach is not adequate for identifying the needs of distinct traveler segments and of travelers using different travel modes. Furthermore, two-thirds of the attributes are consistently classified into the same factor category which entails important policy implications. This research deepens and expands the very limited knowledge of the application of the three-factor theory in the transport field.

Since the refitment of Lidingöbanan which was completed in 2015, the barrier time for car traffic at level crossings have been protracted. In Skärsätra, where Lidingöbanan crosses Södra Kungsvägen and traffic flows are high, this is a particularly big problem. Therefore, it is investigated in this report whether it is possible to shorten the signaling distance for the road protection at the intersection and what measures are required to accomplish this. A literature study has been made, including the history of Lidingöbanan, what a road protection is, what accident hazard exist at level crossings and what laws and regulations apply to road protection. It is shown that the main reason behind the protracted blocking time is the fact that the AGA station is located in the signaling range, which causes the blocking time to vary vigorously and the waiting time tends to be drawn-out.Further along in the report, the pre ringing time and control distance is calculated in order to be able to determine a signaling distance and an aproximate block time for car traffic. One result was shown to be sufficiently short for the signaling range activation point to be after AGA and the blocking time becomes more constant and shorter than in the current situation. The results are analyzed as to what measures have been required to shorten the signaling distance and it is discussed how well the regulations have been complied in the calculations. Lastly, the conclusion is drawn that it is possible to shorten the signaling distance from 656 meters to 577 meters, with this reduction the AGA station is no longer included in the signaling range. The blocking time has decreased from an average of 90 seconds to 61 seconds. It can be noted that the waiting time has diminished thanks to the shortened signaling distance and the fact that the AGA station no longer is located within the signaling range.

Urban bus services e subject to high levels of uncertainty and disturbances. Methods to determine the timetable are designed to absorb variations in riding times between stops by allocating additional travel time. The propagation of service unreliability along the route could be restrained by drivers' adjustment at stops and between stops. This paper analyzes the main determinants of bus riding times deviations based on automatic vehicle location (AVL) data from four trunk lines in Stockholm, Sweden. The analysis indicates that drivers can and do adjust their speeds in response to instantaneous real-time schedule adherence information, although these adjustments depend on the underlying control scheme: locations where the performance is measured. A model for bus riding time deviations was estimated with autoregressive effects, performance indicators, link characteristics, and trip attributes as the explanatory factors. The results can support the development of travel time prediction and real-time control strategies that take drivers' response to operations into account. This highlights the importance of the human factor in designing control schemes and the corresponding transit performance evaluation.

Today’s society in Sweden differs from that 160 years ago when railroads were firstbuilt. In general, people are wealthier and as a result used to a higher degree of comfort,service and independency. Throughout the years, both the train stations and the wagonshave tried to keep up with this development by increasing for instance the comfortonboard regarding the chairs, and developed what could be considered malls inside ofthe train stations.The purpose of this report is to take a closer look on the history of how the typical longdistance train traveler has changed in time, if and how the wagons and stations havedealt with these changes in order to get an idea of how the train wagons and stationsshould be designed in the near future in order to compete with the car or airplane.The traveler today and in the nearest future can be considered to be comfortable and aswe are moving towards a more individualistic society, we wish to travel on our ownrequirements. Both the trains themselves, the infrastructure (railroad and ticket salesfor instance) and the stations play a big part in the traveler’s perception of the journeyall together. Hence, the trains need to adapt, not only to the higher degree ofcomfortable chairs that the passengers wish for, but to the passenger that wants privacy,the families traveling with their children and to all the other individual desires. Aflexible train is a good train in regards to what the passengers request. This applies alsoto the train stations, which in a higher degree than ever need to adapt to thecomfortable, efficient traveler. Making sure that all kind of traffic can arrive and departfrom the station, weather it is car pools, rental bikes or local buses is crucial. Also, it isimportant that the station offers several activities and services that the modern traveler needs in order to be time efficient.

In the existing regulations for Swedish road- and street design (VGU) there are models thatthe Swedish Transport Administration must follow when projecting new roads, including thelayout of the side area and the safety zone. The side area is the area right by the roadwayand the safety zone is the part of the side area that is supposed to be designed to be as safeas possible in order to minimize the risk of severe personal injuries in case of an accident.The current models for the safety zone design are not based on empirical data but ratherresearch from the 1960s as well as a few assumptions. The lack of supporting empirical datain the current road- and street design regulations is inadequate in the area “design of thesafety zone”.The purpose of the project has been to produce a method for developing a connectionbetween the design of the safety zone and the turnout of accidents in order for futureregulations to be based on empirical data.During the course of the project, three different methods have been developed andevaluated. In all methods, accident statistics from Strada (Swedish Traffic Accident DataAcquisition) have been used. For each of the three methods, different tools have been usedin order to analyze the safety zone at the scene of each accident. In method 1, photosdescribing the side area was used. Method 2 featured manual measurements in a pointcloud and method 3 featured semi-automatic measurements from the same point cloud.After evaluating each of the three methods, it was found that method 1 and method 2 didnot give reliable results or measurements and they were therefore rejected. Method 3 canbe used in order to efficiently sort and match accident data with the design of the safetyzone. With a larger dataset, it may be possible to use method 3 on a wider scale and thenperform a regression analysis or statistical evidence to investigate the relationship betweenaccidents and the design of the safety zone

We examine the effect of weather, accessibility and built-environment characteristics on overall travel experience as well as the experience with the latest trips. These are factors that are often disregarded in the travel satisfaction literature even though they are believed to largely influence the first mile of the door-to-door trip. This study fills a research gap in investigating all these factors by using, amongst other, a relatively large travel satisfaction survey from years 2009 to 2015 and by focusing on urban and peri-urban geographical contexts, the city and county of Stockholm (Sweden), respectively. The results show that county dwellers living close to a metro station and in well linked-to-all areas report higher overall travel satisfaction evaluations. In addition, precipitation and ground covered with snow have a negative influence on travel satisfaction. Our findings indicate that built-environment characteristics exert a rather weak influence on the travel experience, especially in the peri-urban context. However, some aspects such as living in areas with medium densities, low income and with high safety perceptions around public transport stations are associated with higher satisfaction levels. In turn, areas with single land uses are found to have lower travel satisfactions. These results are important for public transport planners and designers in devising measures to prevent and mitigate the negative outcome of some weather conditions and to conceive better designed transit oriented developments.

14 ny- eller ombyggda stationer för i första hand fjärrtrafik och långväga regionaltrafik med sedan 1990 kraftigt förbättrat tågutbud har valts ut i studien: Från söder till norr Malmö Hyllie, Triangeln och Malmö C, Laholm, Flemingsberg, Södertälje syd, Läggesta, Strängnäs, Eskilstuna C, Bålsta, Uppsala C, Söderhamn, Umeå Ö och Umeå C. Analysen omfattar tre huvuddelar: Dels 1) en analys av avresande tågresenärers resvanor och värderingar, dels 2) en morfologisk studie över samhällsstrukturens förändringar 1993–2013 och kommunernas översiktsplaner, och för stationerna i Mälardalen dels 3) en modellanalys av förändringar i trafik och tillgänglighet vid alternativ lokalisering.

Sammanfattningsvis tyder studien på att valet av lokalisering av nya stationer får effekter för samhället när det gäller samhällsstruktur, resenärernas nöjdhet, resvanor och färdmedelsval och tillgänglighet till arbetsplatser och service, vilket påverkar järnvägssystemets attraktivitet och därmed reseefterfrågan. Centralt eller urbant lokaliserade stationer framstår därmed som mer attraktiva och bättre ur systemsynpunkt än perifert lokaliserade stationer.

Anledningen till att perifera stationer tillkommer är dock att man vill minska anläggningskostnaderna eller intrång i redan bebyggd miljö jämfört med en urban lokalisering. När detta blir aktuellt är det viktigt med bra anslutande kollektivtrafik och andra stödjande strategier för bland annat exploatering. Föreliggande studie tillför dock argument att värdera de positiva effekterna av en urbant lokaliserad station högre än idag.

Vi har identifierat möjligheter att vidareutveckla metoderna för att utvärdera effekterna av stationslokalisering inom fler områden. Det går också införa analyserna i lokaliseringsutredningar för att förbättra beslutsunderlaget i framtida val av stationslokalisering.

Cost-Benefit Analysis (CBA) is a common tool used when determining whether to invest or not in newpublic transport infrastructure by weighing the costs of the object in question against the benefits it isexpected to bring about. If the benefits exceed the costs, the recommendation would be to invest.Usually, however, the cost exceeds the benefit which makes it difficult to motivate new publictransportation investments.This does, however, not necessarily indicate that most public transportation investments areunprofitable but rather that there exist deficiencies in the tool. Usually a CBA does not include all aspectsnecessary for a proper appraisal and one of those aspects specifically may be the perceived on-boardcrowding.This thesis presents a method for incorporating the effects of perceived on-board crowding into a CBAvia an implementation of a seat-occupancy based crowding multiplier approximation into thegeneralized travel cost function of a static transit assignment model (TAM).A problem with static TAMs is that the underlying travel demand is fixed throughout the analyzed timeperiod.Consequently, only average passenger loads are obtained after an assignment procedure whichmeans that only the average perceived on-board crowding can be observed. A direct implementationwould thus not be sufficient for representing how travelers perceive crowding in real life as traveldemand is highly variable but also due to other load-impacting phenomena such as bus bunching.The solution to this problem was to make the TAM consider the actual perceived on-board crowding byderiving mode-wise expressions of the weighted crowding multiplier as a function of the averagecrowding multiplier. These expressions were further implemented into the generalized travel costfunction imbedded in the static TAM.The enhanced model was applied to a case being the proposed plan of building a new metro betweenÄlvsjö and Fridhemsplan in Stockholm. The results proved that the travel time savings were 70 % largerwhen crowding was considered in the TAM than when it was not. Moreover, the CBA resulted in apositive outcome when crowding was considered as opposed to the CBA in which it was not, which 

indicated a negative outcome.

A lot of challenges arises from an expanding Stockholm. One of them is mentioned in Theregional development plan for the Stockholm region 2010 (RUFS) as “To increase theperceived safety in the region when the world is perceived as more unsafe”(Regionplanenämnden, 2010:19). A typical example of a location where it is difficult tocreate environments that are perceived as safe is the Stockholm subway.The aim of this study is to examine which situational and social factors that contribute to thefeeling of being safe at the subway stations. The literature review covers the concept ofperceived safety and its definition. The social and situational factors that should affectperceived safety according to science are also explained. The factors that are examined in thisreview are the same as the ones in the theory Crime Prevention Through EnvironmentalDesign (CPTED). Data from the Stockholm safety survey from 2014 “Trygghet i dinstadsdel” is used to identify the station that is perceived as the safest, Stora Mossen, and thestation that is perceived as least safe, Tensta.A qualitative field study at the stations Stora Mossen and Tensta in the Stockholm subway areexecuted with support from the literature review and data from the Stockholm safety survey.The field study includes two visits at each station, during day and night, where the social andsituational attributes that contribute to perceived safety are analyzed.Findings show that the physical environment affects the perceived safety at the two examinedsubway stations but that this is not the only contributing factor. The perceived safety in thesurrounding area and the qualities and earlier experiences of the individual also have an effecton the perceived safety at the stations. It is also established that no general conclusions canmed made because merely a qualitative study at limited amount of subway stations wasconducted.

This report discusses how the weather affects the traffic focusing on accidents due to various types of weather and in that case how the weather affects. In this report, weather is used to describe a temporary state of temperature, precipitation and wind at a certain location. The accidents due to the weather have been combined with a several parameters, for example light, level of injury and when the accidents happened to see if this had something to do with the result.Sweden strives for the so-called Zero-vision, which goal is that no one should die or get seriously injured in traffic. As a part of this the purpose of the report is to investigate the effects of weather and if it’s possible to prevent accidents due to weather.The report is built on literature studies, accident data from STRADA and an interview with Folksam. The data has primarily been used to sort out the accidents that seem to be due to weather and then all the accidents have been compared with the ones due to weather in various diagrams. Thereafter, all the different parameters for weather related accidents have been put into crosstabulations to find connections.The investigation is delimited to five municipalities within the Stockholm area in the timeframe of three years and only when cars were involved.From the investigation, I gather that the majority of the accidents are not related to the weather conditions, but rather to our human behaviour and attitude to the current weather. The weather when the accidents happen doesn’t seem to differ very much between all accidents and the ones related to weather. Neither does the level of injury differ. Time of year varies more, as weather related accidents primarily happen during the winter months, but all accidents in total are more divided over the year. The most common circumstances are that the road is slippery or that the driver is blinded by the sun. The most dangerous combination seems to be clear weather and shallow ice with visible road surface, but also rain and a wet road is the cause of many accidents.The literature already includes studies showing that speed and traffic flows decreases with slippery, snowy or icy road surface conditions and the studies also makes a difference between pre, mid and late winter period. Even in the summer the roads can become slippery through both road polishing and bleeding asphalt. In the literature it is also described what effect speed has as well as snow clearance measures. Problems with damaged windshields where scratches creates poor visibility as well as how bad eyesight of the driver reinforces this effect is also discussed.Finally, it is concluded that our behaviour as humans is the prime factor for the outcome of the accidents. How we behave in the presence of current weather and how well prepared we are for the risks which follows different weathers and circumstances will affect how well we can handle these situations.

The paper proposes a generalization of public transport service reliability, incorporating both travel times and travel conditions based on passengers’ perceived journey time. Time is partitioned into waiting and transfer time as well as in-vehicle time under different travel conditions (crowding and seat availability), which may vary along a journey and between days. The experienced service reliability gap (ESRG) index is introduced, defined as the difference between an upper percentile (e.g., the 95th) and the median perceived journey time across days for a particular OD pair and departure time. The metric is evaluated by tracing virtual trips from origin to destination with journey times and travel conditions based on automated vehicle location (AVL) and automated passenger count (APC) data and seated status modelled probabilistically. A study of a high-frequency bus line in Stockholm, Sweden shows that travel conditions co-vary only weakly with nominal journey time, and the ESRG index display patterns across the day not evident in existing reliability measures, such as a wider and later afternoon peak. The ESRG displays significant variation between OD pairs along the line. Correlation with headway variability suggests that measures improving bus regularity have additional positive effects on experienced service reliability.

Nowadays, traffic congestion poses critical problems including the undermined mobility and sustainability efficiencies. Mitigating traffic congestions in urban areas is a crucial task for both research and in practice. With decades of experience in road traffic controls, there is still room for improving traffic control measures; especially with the emerging technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and Big Data. The focus of this thesis lies in the development and implementation of enhanced traffic signal control systems, one of the most ubiquitous and challenging traffic control measures.

This thesis makes the following major contributions. Firstly, a simulation-based optimization framework is proposed, which is inherently general in which various signal control types, and different simulation models and optimization methods can be integrated. Requiring heavy computing resources is a common issue of simulation-based optimization approaches, which is addressed by an advanced genetic algorithm and parallel traffic simulation in this study.

The second contribution is an investigation of an intelligent local control system. The local signal control operation is formulated as a sequential decision-making process where each controller or control component is modeled as an intelligent agent. The agents make decisions based on traffic conditions and the deployed road infrastructure, as well as the implemented control scheme. A non-parametric state estimation method and an adaptive control scheme by reinforcement learning (RL) are introduced to facilitate such an intelligent system.

The local intelligence is expanded to an arterial road using a decentralized design, which is enabled by a hierarchical framework. Then, a network of signalized intersections is operated under the cooperation of agents at different levels of hierarchy. An agent at a lower level is instructed by the agent at the next higher level toward a common operational goal. Agents at the same level can communicate with their neighbors and perform collective behaviors.

Additionally, a multi-objective RL approach is in use to handle the potential conflict between agents at different hierarchical levels. Simulation experiments have been carried out, and the results verify the capabilities of the proposed methodologies in traffic signal control applications. Furthermore, this thesis demonstrates an opportunity to employ the systems in practice when the system is programmed on an intermediate hardware device. Such a device can receive streaming detection data from signal controller hardware or the simulation environment and override the controlled traffic lights in real time.

Increasing traffic congestion poses significant challenges for urban planning and management in metropolitan areas around the world. One way to tackle the problem is to resort to the emerging technologies in artificial intelligence. Traffic light control is one of the most traditional and important instruments for urban traffic management. The present study proposes a traffic light control system enabled by a hierarchical multi-agent modeling framework in a decentralized manner. In the framework, a traffic network is decomposed into regions represented by region agents. Each region consists of intersections, modeled by intersection agents who coordinate with neighboring intersection agents through communication. For each intersection, a collection of turning movement agents operate individually and implement optimal actions according to local control policies. By employing a reinforcement learning algorithm for each turning movement agent, the intersection controllers are enabled with the capability to make their timing decisions in a complex and dynamic environment. In addition, the traffic light control operates with an advanced phase composition process dynamically combining compatible turning movements. Moreover, the collective operations performed by the agents in a road network are further coordinated by varying priority settings for relevant turning movements. A case study was carried out by simulations to evaluate the performance of the proposed control system while comparing it with an optimized vehicle-actuated control system. The results show that the proposed traffic light system, after a collective machine learning process, not only improves the local signal operations at individual intersections but also enhances the traffic performance at the regional level through coordination of specific turning movements.

In freight transport systems, fuel consumption can be significantly reduced by means of heavy-duty vehicle (HDV) platooning on highways. An HDV platoon refers to a group of HDVs with small intermediate distances enabled by the HDVs being equipped by sensors and controllers. It is of importance for transport authorities and industries to explore the effects on overall traffic systems by introducing HDV platooning. Although previous studies have investigated the potential benefits of HDV platooning, the control performance and effects in real traffic have barely been explored. In the present study, a simulation platform has been developed to model and analyze the effects of HDV platoons in real traffic conditions. The simulation model is based on an open-source microscopic traffic simulator, SUMO, and calibrated using data collected by a motorway control system (MCS). The current model incorporates the vehicle dynamics of HDVs in the simulation, while an HDV in a platoon is controlled by a proportional-integral-derivative (PID) controller for its longitudinal behavior. Furthermore, the PID control parameters have been optimized for a driving cycle, according to predefined criteria, while taking vehicle dynamics and stability conditions into account. A case study has been carried out by adopting HDV platooning on a highway stretch in Sweden. The performance of the HDV platoons and effects on the other vehicles on the highway have been evaluated for different scenarios through multiple simulation runs. As a result, it is found that substantial fuel reductions have been achieved for HDVs if they form platoons in the evaluation cases. The analysis of the other vehicles shows only rather small effects when HDV platooning is implemented.

The demand for road freight transport continues to grow with the growing economy, resulting in increased fossil fuel consumption and emissions. At the same time, the fossil fuel use needs to decrease substantially to counteract the ongoing global warming. One way to reduce fuel consumption is to utilize emerging intelligent transport system (ITS) technologies and introduce heavy-duty vehicle (HDV) platooning, i.e. HDVs driving with small inter-vehicle gaps enabled by the use of sensors and controllers. It is of importance for transport authorities and industries to investigate the effects of introducing HDV platooning. Previous studies have investigated the potential benefits, but the effects in real traffic, both for the platoons and for the surrounding vehicles, have barely been explored. To further utilize ITS and optimize the platoons, information about the traffic situation ahead can be used to optimize the vehicle trajectories for the platoons. Paper I presents a dynamic programming-based optimal speed control including information of the traffic situation ahead. The optimal control is applied to HDV platoons in a deceleration case and the potential fuel consumption reduction is evaluated by a microscopic traffic simulation study with HDV platoons driving in real traffic conditions. The effects for the surrounding traffic are also analysed. Paper II and Paper III present a simulation platform to assess the effects of HDV platooning in real traffic conditions. Through simulation studies, the potential fuel consumption reduction by adopting HDV platooning on a real highway stretch is evaluated, and the effects for the other vehicles in the network are investigated.

Here, we propose a method to design a charging infrastructure for a fleet of electric vehicles such as a fleet of taxicabs, fleet of vans used in the city logistics or a fleet of shared vehicles, operating in large urban areas. Design of a charging infrastructure includes decisions about charging stations location and number of charging points at each station. It is assumed that the fleet is originally composed of vehicles equipped with an internal combustion engine, however, the operator is wishing to replace them with fully electric vehicles. To avoid an interaction with other electric vehicles it is required to design a private network of charging stations that will be specifically adapted to the operation of a fleet. It is often possible to use GPS traces of vehicles characterizing actual travel patterns of individual vehicles. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to the data characterizing a large taxicab fleet operating in the city of Stockholm. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure. 

We propose optimization model to design a charging infrastructure for a fleet of electric vehicles. Applicable examples include a fleet of vans used in the city logistics, a fleet of taxicabs or a fleet of shared vehicles operating in urban areas. Fleet operator is wishing to replace vehicles equipped with an internal combustion engine with fully electric vehicles. To eliminate interaction with other electric vehicles it is required to design a private network of charging stations that is specifically adjusted to the fleet operation. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to data characterizing a large taxicab fleet operating in the city of Stockholm. The model assumes that all vehicles posses complete information about all other vehicles. To study the role of available information, we evaluate the resulting designs considering the coordinated charging when vehicle drivers, for example, reveal to each other departure times, and the uncoordinated charging when vehicle drivers know only actual occupation of charging points. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure.

Development of major shopping centres continues even though online shopping is increasing. This has implications for mode and destination choice for shopping travel and therefore also for sustainability, which need to be considered in planning policy. In this paper, we estimate models for shopping travel using an unusually rich data set of shopping attractions. We find that shopping travel is best represented in three separate models: consumables in short and long activity segments and durables. In all of these models, we show that representing nearby attractions outside the destination zone adds to the measured attraction. For long activity consumables and for durables, the addition of secondary attractions within 2 km of the main destination gives the best models. For short activity consumables, both 2 km and 5 km add to the model, but 5 km is slightly better. Furthermore, we find significant within-zone correlation in the consumables models but are unable to find significant between-zone correlation, indicating that zone boundaries have some behavioural meaning for shopping travellers, but larger areas are not viewed in this way. Shopping attractions with a specifically Swedish impact, Systembolaget (official alcohol outlet in Sweden) and IKEA, proved to be important in all the models. These attractors work better as part of the size than as part of the utility, indicating that they appear to be separate attractors of trips, rather than as adding to the utility of other attractors. The models are also applied in two policy scenario analyses in which the impacts of new IKEA establishments and availability of Systembolaget in all zones on destination and mode choice are assessed.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources.

During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.

Intercity travel congestion during the main national holidays takes place every year at different places around the world. Charge reduction measurements on existing toll roads have been implemented to promote an efficient use of the expressways and to reduce congestion on the public transit networks. However, some of these policies have had negative effects. A more comprehensive understanding of the determinants of holiday intercity travel patterns is critical for better policymaking. This paper aims to investigate the effectiveness of the road toll discount policy on mode choice behavior for intercity travel. A mixed logit model is developed to model the mode choices of intercity travelers, which is estimated based on survey data about intercity journeys from Beijing during the 2017 Chinese Spring Festival holiday. The policy impact is further discussed by elasticity and scenario simulations. The results indicate that the expressway toll discount does increase the car use and decrease the public transit usage. Given the decreased toll on expressways, the demand tends to shift from car to public transit, in an order of coach, high-speed rail, conventional rail, and airplane. When it comes to its effect on socio-demographic groups, men and lower-income travelers are identified to be more likely to change mode in response to variation of road toll. Finally, policy effectiveness is found to vary for travelers in different travel distance groups. Conclusions provide useful insights on road pricing management.

De samhällsekonomiska kalkylerna tillmäts stor vikt särskilt av nationalekonomer medan politikerna inte alltid fattar beslut i enlighet med resultaten av dessa. En avgörande input till de samhällsekonomiska kalkylerna är trafikprognoserna. I denna rapport görs därför en genomgång av Trafikverkets prognoser för höghastighetsbanor och jämförelser med internationella erfarenheter av snabba tågförbindelser.

Det är uppenbart att Trafikverkets prognoser underskattar resandet som följd av höghastighetståg. Prognosen utan höghastighetsbanor ger en mycket hög marknadsandel för tåg och skillnaden med höghastighetsbanor blir därför liten. I Trafikverkets prognoser kommer en mindre del från bil och flyg medan större delen större är nya resor. Någon modell för utrikesresor används inte och kombinerade resor med flyg och tåg kan inte prognosticeras. Det är inte säkert att Sampers ger korrekta resultat för dessa analyser. Det är inte heller säkert att modellen ger tillräckligt bra underlag för att analysera var tågen ska stanna, lokalisering av stationer m.m.

Prognosen i sig har också stor betydelse för planeringen av höghastighetsbanorna, för dimensioneringen av utbudet, för bedömning av möjligheterna till medfinansiering från operatörer och intressenter och för planering av framtida utbyggnader av annan infrastruktur som flygplatser och vägar för att nämna några exempel.

Genom den utbudsdatabas som KTH:s Järnvägsgrupp successivt har byggt upp finns en detaljerad beskrivning av hur utbudet av tåg, flyg och buss ser ut och också tidsseriedata för perioden 1990-2017. Däremot saknas detaljerad information av efterfrågan. Syftet med projektet ”Resandeflöden” är att ta fram data så att efterfrågan av tågtrafik kan beskrivas på ett bättre sätt.

Genom att kombinera efterfrågedata med detaljerade utbudsdata och går det att få en bra bild över persontrafiken på järnväg i Sverige. Kompletterat med databaser över trafik- och fordon under en mätdag kan man beräkna nyckeltal och dygnsvärden kan skrivas upp till årsvärden. Tågtrafiken i Sverige kan då beskrivas t.ex. i form av antalet tågkilometer, personkilometer och beläggningsgrader för olika typer av trafik t.ex. lokaltrafik, regionaltrafik, Intercity-trafik och snabbtåg. I projektet visas att modellen har en god överensstämmelse mellan verkligt resande ner på linje- och produktnivå.

KTH Järnvägsgruppen har 2018 fortsatt genomfört ett uppdrag att beskriva utvecklingen av utbud, priser på järnvägslinjer i Sverige sedan 1990. I detta ingår också att beskriva effekterna av avregleringen och konkurrensen mellan olika transportmedel i långväga trafik. Sedan 2015 ingår också en allmän beskrivning av utvecklingen på transportmarknaden och järnvägens betydelse.

En metod för att beräkna hur den potentiella tillgängligheten påverkas av restider, priser och förseningar har utvecklats. Resenärernas uppoffring i form av en generaliserad kostnad kan räknas ut för varje linje för perioden 1990-2018. Hur mycket förseningarna bidrar till att försämra tillgängligheten var inte tidigare känt. En analys av utvecklingen under hela perioden 1990-2018 redovisas i denna rapport. En sammanställning av data redovisas i en särskild tabellbilaga och i en databas.

Samband mellan den ekonomiska utvecklingen och transporterna

• Det finns ett starkt samband mellan den privata konsumtionen och resandet
• Efter 1990 har inrikesresandet ökat långsammare än den privata konsumtionen
• Per invånare har resandet med bil stagnerat, med tåg ökat och med inrikesflyg varit konstant
• Utrikesresorna med flyg har dock ökat dubbelt så snabbt som den privata konsumtionen 
• Tar man hänsyn till svenskarnas resor med utrikes flyg har resandet per invånare ökat

Järnvägens utveckling i ett långsiktigt perspektiv

• Privatbilens expansion 1950-1970 och minskad tågtrafik
• Energikriserna 1974 och 1979 ledde till ökad tågtrafik
• Flygets expansion under 1980-talet medförde stagnerande tågtrafik
• Investeringar i järnvägar och nya tåg 1990-2010 gav fördubblad tågtrafik
• Kvalitetsproblem från 2010 som följd av ökad trafik och eftersatt underhåll

Järnvägsnätets utnyttjande och järnvägens produktivitet

• Antalet körda persontåg har ökat från 18 till 36 per km bana och dag 1990-2017
• Det åker i genomsnitt 3800 personer per km bana och dag 2017 vilket motsvarar 150 bussar
• Oförändrat 10 godståg per km bana och dygn 1990-2017, godsmängden har ökat med 18 %
• Det transporteras 6300 ton per km bana och dag 2017 motsvarande 210 lastbilar

Utvecklingen 1990-2018

• Det går 120 % fler tåg som går 20 % snabbare 2018 än 1990
• Priserna har varit stabila men prisdifferentieringen har ökat
• Tågresandet har ökat med 102 % från 1990 till 2017
• Resandet med regionaltåg har ökat med 214 % och med fjärrtåg 50 %

Utveckling av punktligheten 2001-2017

• Punktligheten för alla tåg var högst 2004 med 93 %, lägst 2011 med 89 % och var 90 % 2017
• Punktligheten har ännu inte kommit upp i samma nivå som före kvalitetskrien 2010-2011
• Punktligheten är beroende på linjelängd men förseningen per tågkilometer är konstant
• Punktlighetsmålet på 95 % blir svårt att uppnå med dagens infrastruktur och trafik

Effekter av konkurrensen mellan transportmedel

• Hård konkurrens inom inrikesflyget sedan 1994 – stabil på de största linjerna
• Konkurrens mellan flygbolag och flygplatser ger labilt utbud på de mindre linjerna
• Flygskatten på 60 kr utgör ca 4 % på en inrikes flygresa
• Konkurrens i långväga busstrafik sedan 1997 huvudsakligen med lågt pris
• Flixbuss köpte Swebus 2018, ökade utbudet och sänkte priserna

Utveckling av tågutbud 2017-2018

• Snälltåget kör fler turer och under 5 timmar Malmö−Stockholm med tåg som går i 200 km/h
• Fortsatt konkurrens Göteborg−Stockholm; MTR har kortat restiderna med ca 8 minuter
• Nytt trafikupplägg på pendeltågen i Stockholm med utökad trafik bl.a. som följd av Citybanan
• Nytt periodkort i Mälardalen med Movingo, gäller både på tåg och anslutande kollektivtrafik
• Uppsala−Stockholm: SJ kör halvtimmestrafik och direkttåg på 30 min varje timme
• SJ:s nattåg till Jämtland som minskades 2016 har åter börjat köras dagligen med statligt stöd

Traffic signals play an important role in a citys traffic system both as an instrument fordistribution of space and for creating safety. When planning traffic signals there are a numberof different forms of control that can be used. One of there is coordinated control where anumber of traffic signals are planned in union in order to create a green wave along an arterialroad.The green wave works under the assumption that the traffic travelling on the link is keepingthe appropriate speed. Therefore there is also a demand that the planner has the correctinformation about the speed distribution of the vehicle composition using the system.In Stockholm there is a political will to prioritize walking and cycling as modes of transportahead of private transport by car in the planning. This is expressed in the official documentexplaining the strategies for mobility (Sv. “Framkomlighetsstrategin”). An important tool inmanaging to make the change in the traffic system that this would entail is traffic signaltiming. The green waves that have been planned historically have been planned for theautomobile for the speeds that are relevant for this mode of transport without necessarilypaying much attention to the effects this would have on cyclists.Götgatan is a street in central Stockholm that is an important connection for the cyclistscommuting from the southern suburbs to the central part of the city. In 2014 the municipalitydecided to rebuild this street to widen the bike lanes and implement a green wave designed tobe appropriate for a cyclist travelling at a speed of roughly 18 km/h. There was no hard datato support this assumed speed and therefore the efficacy of the wave is also uncertain for theactual cyclists using the street. Therefore this project has been directed at measuring the actualdistribution of velocities found in the afternoon rush. This was done for normal cyclists andelectrically assisted cyclists to try to quantify the effect of an increased share of electricalbicycles through micro simulation in Vissim. Vissim was also used to assess the delay causedby the traffic signals. In total the speed of 2200 cyclists was collected over three hoursdistributed over three days in april.The findings are that the average velocity of all cyclists was 21.9 km/h with a standarddeviation of 4.5 km/h.For electrical bicycles the average speed was 23.8 km/h and the standard deviation was 3.1km/h.For normal bicycles the average speed was 21.7 and the standard deviation was 4.5 km/h.The main effect of an increasing share of electrical bicycles does not seem to be a largeincrease of speed but rather a decreased spread of the distribution of speeds.The green wave does seem to be working well for the speeds found in this study and thedifference in delay found when removing the signals in the coordination was marginal.One possible reason for this might be that the green splits are planned in favor of the trafficalong the road in order for the car-traffic on the link to be able to move through the street.

This paper explores the interactions between travel demand, time allocation and mode choice in different seasons by jointly modeling the work and/or study, routine and leisure activity-travel engagements of 67 individuals in Stockholm, Sweden. A longitudinal panel two-week travel diary data collected in four consecutive waves over a span of seven months period that covers all four different seasons; autumn, winter, spring and summer, were analysed by using simultaneous Tobit models. The model was applied to explore the interactions among each activity-travel indicator, and individuals' unique characteristics and endogeneity in activity-travel engagements between different seasons were also considered in the model system. The results of models reveal clear trade-offs between mandatory activities (work and/or study) and non-mandatory activities (routine and leisure), regardless of any seasons, although the magnitudes vary between seasons. There is also a positive mutual endogeneity relationship between number of trips and activity duration within the same activity type. The trade-offs between work and/or study trips towards routine and leisure trips are larger in winter and spring respectively, than in other seasons. It is also found that mode effects on travel time for conducting mandatory activity are much larger in spring than in other seasons. However, the effects of public transport and slow modes on travel time for leisure activities are much larger in summer than in other seasons.

Congestion on urban and freeway networks has become a major problem, leading to increased travel times and reduced traffic safety. In order to suggest traffic management solutions to improve the transport system efficiency, it is important to capture the travel demand patterns, expressed as origin-destination (OD) matrices, and understand the mechanisms of traffic bottlenecks. The increasing availability of traffic data offers significant opportunities to effectively address these issues. The thesis uses heterogeneous traffic data to improve three important problems.

The first problem relates to the dynamic OD estimation problem, which entails significant challenges due to its complexity. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been commonly used to solve the problem, which can handle any available data that can improve the estimation accuracy. However, it encounters stability and convergence issues. The thesis proposes a general modification of SPSA, called cluster-wise SPSA (c-SPSA), that has more robust performance and finds better solutions. Its efficiency is demonstrated through simulation experiments for a network from Stockholm.

The second problem focuses on the development of methods for utilizing heterogeneous traffic data for the analysis and management of freeway work zone and tunnel bottlenecks. Simulation is used as the means to evaluate and optimize various mitigation strategies for each case.

The third problem analyzes multimodal impacts due to network disruptions for the case of tunnel bottlenecks, using a data-driven approach. Tunnel congestion is often dealt with temporary closures, which may cause significant disruptions. It is crucial to identify the potential multimodal impacts of such interventions so as to design efficient and proactive mitigation strategies. The thesis shows the benefits of combining multiple data sources to analyze the impacts of temporary tunnel closures for a freeway tunnel in Stockholm.

Tunnel congestion is an important safety problem and is often dealt with using disruptive traffic management strategies, such as closures. The paper proposes an approach to identify the underlying causes of recurrent congestion in tunnels and tests the hypothesis that the cause may vary from day to day. It also suggests that the appropriate tunnel management strategy to deploy depends on the cause. Utilizing traffic sensor data the approach consists of: (i) cluster analysis of historical traffic data to identify distinct congestion patterns; (ii) in-depth analysis of the underlying demand patterns and associated bottlenecks; (iii) simulation to evaluate alternative strategies for each demand pattern; (iv) on-line classification analysis which is able to identify, in real time, the emerging congestion pattern, and inform the type of mitigation strategy to be implemented. The methodology is demonstrated for a congested tunnel in Stockholm, Sweden revealing two different spatiotemporal congestion patterns. The results show that, if the current strategy of closures is to be used, the timing should depend on the congestion pattern. However, metering is the most promising strategy. The on-line classification of the emerging congestion pattern is effective and can inform appropriate strategy proactively. The analysis emphasizes that the effectiveness of tunnel traffic management can be increased by identifying the causes of congestion on a given day. 

Tunnel congestion is an important safety problem and is often dealt with using disruptive traffic management strategies, such as closures. The paper proposes an approach to identify the underlying causes of recurrent congestion in tunnels and tests the hypothesis that the cause may vary from day to day. It also suggests that the appropriate tunnel management strategy to deploy depends on the cause. Utilizing traffic sensor data the approach consists of: (i) cluster analysis of historical traffic data to identify distinct congestion patterns; (ii) in-depth analysis of the underlying demand patterns and associated bottlenecks; (iii) simulation to evaluate alternative strategies for each demand pattern; (iv) on-line classification analysis which is able to identify, in real time, the emerging congestion pattern, and inform the type of mitigation strategy to be implemented. The methodology is demonstrated for a congested tunnel in Stockholm, Sweden revealing two different spatio-temporal congestion patterns. The results show that, if the current strategy of closures is to be used, the timing should depend on the congestion pattern. However, metering is the most promising strategy. The on-line classification of the emerging congestion pattern is effective and can inform appropriate strategy proactively. The analysis emphasizes that the effectiveness of tunnel traffic management can be increased by identifying the causes of congestion on a given day.

Tunnel congestion is an important safety problem and is often dealt with using disruptive traffic management strategies, such as closures. The paper proposes an approach to identify the underlying causes of recurrent congestion in tunnels and tests the hypothesis that the cause may vary from day to day. It also suggests that the appropriate tunnel management strategy to deploy depends on the cause. Utilizing traffic sensor data the approach consists of: (i) cluster analysis of historical traffic data to identify distinct congestion patterns; (ii) in-depth analysis of the underlying demand patterns and associated bottlenecks; (iii) simulation to evaluate alternative strategies for each demand pattern; (iv) on-line classification analysis which is able to identify, in real time, the emerging congestion pattern, and inform the type of mitigation strategy to be implemented. The methodology is demonstrated for a congested tunnel in Stockholm, Sweden revealing two different spatiotemporal congestion patterns. The results show that, if the current strategy of closures is to be used, the timing should depend on the congestion pattern. However, metering is the most promising strategy. The on-line classification of the emerging congestion pattern is effective and can inform appropriate strategy proactively. The analysis emphasizes that the effectiveness of tunnel traffic management can be increased by identifying the causes of congestion on a given day. 

The paper explores the utilization of heterogeneous data sources to analyze the multimodal impacts of transport network disruptions. A systematic data-driven approach is proposed for the analysis of impacts with respect to two aspects: (a) spatiotemporal network changes, and (b) multimodal effects. The feasibility and benefits of combining various data sources are demonstrated through a case study for a tunnel in Stockholm, Sweden which is often prone to closures. Several questions are addressed including the identification of impacted areas, and the evaluation of impacts on network performance, demand patterns and performance of the public transport system. The results indicate significant impact of tunnel closures on the network traffic conditions due to the redistribution of vehicles on alternative paths. Effects are also found on the performance of public transport. Analysis of the demand reveals redistribution of traffic during the tunnel closures, consistent with the observed impacts on network performance. Evidence for redistribution of travelers to public transport is observed as a potential effect of the closures. Better understanding of multimodal impacts of a disruption can assist authorities in their decision-making process to apply adequate traffic management policies.

The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been used for solving the off-line dynamic origin-destination (OD) estimation problem. While the algorithm can be used with very general formulations of the problem, it can also be unstable. The paper proposes methods and evaluates their effectiveness in improving the SPSA performance at two levels: a) scaling the step size and using a hybrid gradient estimation; and b) proposing alternative clustering strategies to be used with the c-SPSA version of the algorithm, where OD flows are estimated in clusters. The proposed enhancements are evaluated through simulation experiments on a real network.

The paper explores the utilization of heterogeneous data sources to analyze the multimodal impacts of transport network disruptions. A systematic data-driven approach is proposed for the analysis of impacts with respect to two aspects: (a) spatiotemporal network changes, and (b) multimodal effects. The feasibility and benefits of combining various data sources are demonstrated through a case study for a tunnel in Stockholm, Sweden which is often prone to closures. Several questions are addressed including the identification of impacted areas, and the evaluation of impacts on network performance, demand patterns and performance of the public transport system. The results indicate significant impact of tunnel closures on the network traffic conditions due to the redistribution of vehicles on alternative paths. Effects are also found on the performance of public transport. Analysis of the demand reveals redistribution of traffic during the tunnel closures, consistent with the observed impacts on network performance. Evidence for redistribution of travelers to public transport is observed as a potential effect of the closures. Better understanding of multimodal impacts of a disruption can assist authorities in their decision-making process to apply adequate traffic management policies.

Many railway networks suffer from high capacity utilisation. For scheduling all services, adjustments to the desired slots are often needed. Such adjustments might lead to longer travel times, crowded trains, longer waiting times for boarding and for transfers. All of this has an important socio-economic impact on both travellers and train operators. This raises the question of the socio-economic assessment of changes in commuter train timetables including transfers. Thus, the aim of this study is to evaluate the effects of adjustments of commuter train timetables on the traveller (i.e. consumer costs) and the train operator (i.e. producer costs). These costs are estimated based on all train trips and operations in the network. In a case study, the effect of changes in departure times (resulting in non-regular interval timetables) is analysed. Further, the price of cancelling a two-way service during different times of the day is compared. The results show that changing departure times can both decrease and increase the total costs, and that regularity for parallel services might not be as important as expected if it is kept for each separate service. For the second study, waiting times for transfers were indicated to have a (too) large impact which can lead to misleading results and might be adjusted in future work. The model is adequate for such kind of questions but needs some more adjustments. For railway networks with dense and heterogeneous traffic (as is the case in Sweden), the contributions of this model are useful for making the challenging timetabling process easier and commuter train services less costly.

There is a lack of understanding of how certain characteristics of the urban environment influence an individual's spatial cognition and familiarity with surrounding areas, and, subsequently, their travel behaviours and how these change over time. This paper aims to address this research gap in exploring the dynamics of individuals' spatial cognitions by observing the changes of respondents' familiar areas over time, and investigating the possible determinants that constitute respondents' familiar areas. Panel data, containing two-week travel diaries and maps of familiar areas, were collected in four different waves over a seven-month period for 55 individuals in Stockholm, Sweden. The reported familiar areas for each individual were digitised into quantifiable variable form and further analysed by applying dynamic binary probit and linear regression models. The results show that, while familiar area is largely influenced by one's previous knowledge of the area, it is also continuously corrected by events in between. Different land use characteristics have different impacts on different social groups' travel patterns, thus contributing to the variability in the size of one's familiar areas.