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New Opportunities in Urban Transport Data: Methodologies and Applications
KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.ORCID iD: 0000-0002-0089-6543
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The deployment of Information and Communication Technologies (ICT) is growing in transportation which may contribute to a more efficient and effective service. The data acquired from ICT based systems could be used for many purposes such as statistical analysis and behavior learning and inference. This dissertation addresses the question of how transportation data that was collected for a specific application can be used for other applications. This thesis consists of five separate papers, each addressing a subset of the topic.

The first paper estimates a route choice model using sparse GPS data. This paper demonstrates the feasibility of an Indirect Inference based estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.

The second paper presents an estimator for the mean speed and travel time at network level based on indirect inference when the data are spatially and temporally sparse.

The third paper proposes an evaluation framework which outlines a systematic process to quantify and assess the impacts of public transport preferential measures on service users and providers in monetary terms, using public transport data sources.

In the fourth and fifth papers, a methodology is developed and implemented for integrating different prediction models and data sources while satisfying practical requirements related to the generation of real-time information. Then the performance of the proposed prediction method is compared with the prediction accuracy obtained by the currently deployed methods.

Abstract [sv]

Användandet av informations- och kommunikationsteknologier (eng. ICT) ökar inom transportområdet, vilket kan bidra till ökad effektivitet. Insamlad data från system med ICT skulle kunna användas för många ändamål såsom statistisk analys, beteendeinlärning och inferens. Denna avhandling tar upp frågan om huruvida transportdata insamlat för en viss tillämpning kan användas för andra. Avhandlingen innehåller fem forskningsartiklar, var och en inriktar sig på sin del av ämnet.

De två första uppsatserna fokuserar på konsistenta estimatorer för hastighet på länkar och ruttval. För många olika tillämpningar är det viktigt att förutsäga en observerad rutts fortsättning, och, givet att det är glest med data, att även avgöra var individen (eller fordonet) har varit. Att skatta den upplevda restiden (och nyttan) av en vald rutt är ett statistiskt svårt skattningsproblem av flera olika skäl. För det första är valmängden ofta mycket stor. För det andra kan det vara viktigt att ta hänsyn till korrelationen mellan de (generaliserade) kostnaderna för olika rutter och därigenom tillåta realistiska ersättningsmönster. För det tredje, på grund av överväganden gällande teknik och den personliga integriteten, kan data vara temporalt och spatialt gles och med endast partiellt observerade rutter. Slutligen, kan det finnas mätfel av fordonens position. Vi utvecklar en estimator för upplevd nytta av en rutt (i den första artikeln) samt för restid på länkar i vägnätverket (i den andra artikeln).

I den tredje artikeln föreslås ett ramverk för utvärdering innefattande en systematisk process som kvantifierar och bedömer inverkan av preferensstyrmedel inom kollektivtrafiken på tjänsteanvändare och leverantörer.

I fjärde och femte artikeln utvecklas och implementeras en metodologi för att integrera olika prediktiva modeller och data-källor i beaktande av praktiska krav kopplade till skapandet av realtidsinformation. Den resulterande prediktionsmetoden jämförs med de metoder som i nuläget används i Stockholm och Brisbane.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. , xvii, 24 p.
Series
TRITA-TSC-PHD, 15:008
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-177489ISBN: 978-91-87353-80-2 (print)OAI: oai:DiVA.org:kth-177489DiVA: diva2:872973
Public defence
2015-12-11, L1, Drottning Kristinas väg 30, KTH, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20151123

Available from: 2015-11-23 Created: 2015-11-20 Last updated: 2015-11-23Bibliographically approved
List of papers
1. Estimating flexible route choice models using sparse data
Open this publication in new window or tab >>Estimating flexible route choice models using sparse data
2012 (English)In: Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, IEEE conference proceedings, 2012, 1215-1220 p.Conference paper, Published paper (Refereed)
Abstract [en]

GPS and nomad devices are increasingly used to provide data from individuals in urban traffic networks. In many different applications, it is important to predict the continuation of an observed path, and also, given sparse data, predict where the individual (or vehicle) has been. Estimating the perceived cost functions is a difficult statistical estimation problem, for different reasons. First, the choice set is typically very large. Second, it may be important to take into account the correlation between the (generalized) costs of different routes, and thus allow for realistic substitution patterns. Third, due to technical or privacy considerations, the data may be temporally and spatially sparse, with only partially observed paths. Finally, the position of vehicles may have measurement errors. We address all these problems using a indirect inference approach. We demonstrate the feasibility of the proposed estimator in a model with random link costs, allowing for a natural correlation structure across paths, where the full choice set is considered.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Keyword
Correlation structure, Flexible routes, Indirect inference, Random links, Sparse data, Statistical estimation, Substitution patterns, Urban traffic networks
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-115858 (URN)10.1109/ITSC.2012.6338676 (DOI)000312599600201 ()2-s2.0-84871199918 (Scopus ID)978-146733064-0 (ISBN)
Conference
2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012; Anchorage, AK; 16 September 2012 through 19 September 2012
Note

QC 20130123. QC 20160214. QC 20160221

Available from: 2013-01-15 Created: 2013-01-15 Last updated: 2016-02-21Bibliographically approved
2. Consistently estimating link speed using sparse GPS data with measured errors
Open this publication in new window or tab >>Consistently estimating link speed using sparse GPS data with measured errors
2014 (English)In: Transportation: Can we do more with less resources? – 16th Meeting of the Euro Working Group on Transportation – Porto 2013, Elsevier, 2014, 829-838 p.Conference paper, Published paper (Refereed)
Abstract [en]

Data sources using new technology such as the Geographical Positioning System are increasingly available. In many different applications, it is important to predict the average speed on all the links in a network. The purpose of this study is to estimate the link speed in a network using sparse GPS data set. Average speed is consistently estimated using Indirect Inference approach. in the end, the Monte Carlo evidence is provided to show that the results are consistent with parameter estimates.

Place, publisher, year, edition, pages
Elsevier, 2014
Series
Procedia Social and Behavioral Sciences, ISSN 1877-0428 ; 111
Keyword
Travel time, Sparse GPS data, Indirect inference, Map matching, route choice
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-122284 (URN)10.1016/j.sbspro.2014.01.117 (DOI)000335582500085 ()
Conference
16th Euro Working Group on Transportation, Porto, Portugal, 4-6 September 2013.
Note

QC 20140613. Updated from manuscript to proceedings paper. QC 20160214. QC 20160221

Available from: 2013-05-17 Created: 2013-05-17 Last updated: 2016-02-21Bibliographically approved
3. Evaluating the Performance and Benefits of Bus Priority, Operation and Control Measures
Open this publication in new window or tab >>Evaluating the Performance and Benefits of Bus Priority, Operation and Control Measures
2016 (English)In: Proceedings of the 95th Transportation Research Board Annual Meeting, Washington DC., 2016Conference paper, Published paper (Refereed)
Abstract [en]

Preferential measures are designed and implemented to improve public transport performance and level-of-service. In the case of urban bus systems, priority, operational and control measures are aimed to elevate bus services to buses with high level of service (BHLS). Even though there is an explosive growth in preferential measures implementation and growing research interest in investigating their impact on performance indicators, there is lack of a systematic evaluation of their benefits. We present an evaluation framework and a detail sequence of steps for quantifying the impacts of public transport preferential measures. The effects of service performance on travel times and costs are assessed by accounting for relations between reliability and waiting times, crowding and perceived travel times, and vehicle scheduling and operational costs. The evaluation integrates the implications of reliability on generalized passenger travel costs and operational costs. We deploy the proposed evaluation framework to a field experiment in Stockholm where a series of measures were implemented on the busiest bus line. The results suggest that the total passenger and operator benefits amount to 47 million Swedish crowns on an annual basis. The overall assessment of the impacts of preferential measures enables the comparison of different implementations, assess their effectiveness, prioritize alternative measures and provide a sound basis for motivating investments.

National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-177485 (URN)
Conference
The 95th Transportation Research Board Annual Meeting, Washington DC.
Note

QC 20160226

Available from: 2015-11-20 Created: 2015-11-20 Last updated: 2016-04-07Bibliographically approved
4. Real-Time Bus Departure Time Predictions: Vehicle Trajectory and Countdown Display Analysis
Open this publication in new window or tab >>Real-Time Bus Departure Time Predictions: Vehicle Trajectory and Countdown Display Analysis
2014 (English)In: 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), IEEE conference proceedings, 2014, 2556-2561 p.Conference paper, Published paper (Refereed)
Abstract [en]

Uncertainty is an important challenge in operating bus systems. Accurate real-time predictions can therefore facilitate adaptive decision making process of both operations and passengers. This scheme should be tractable, fast and reliable to be used in real time applications. This paper presents a hybrid prediction scheme to generate real-time information concerning downstream vehicle trajectories and next bus arrival. The prediction generated by the proposed hybrid scheme integrates three travel time components: schedule, instantaneous and historical data. Genetic algorithm is applied in order to specify the contribution of each data source component to the prediction scheme. The benefits, transferability and estimation form of the proposed scheme were tested by applying it on three trunk bus lines in Stockholm, Sweden. Its performance was compared to a commonly deployed scheme. The results indicate that the proposed scheme reduces significantly the overall mean absolute error for all routes from both operators' and passengers' perspectives.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-165856 (URN)10.1109/ITSC.2014.6958099 (DOI)000357868702101 ()2-s2.0-84937134733 (Scopus ID)978-14799-6078-1 (ISBN)
Conference
The 17th International IEEE conference on Intelligent Transportation Systems (ITSC), 8-11 Oct. 2014, Qingdao, China
Note

QC 20150512. QC 20160214. QC 201060221

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2016-02-21Bibliographically approved
5. A Hybrid Scheme for Real-Time Prediction of Bus Trajectories
Open this publication in new window or tab >>A Hybrid Scheme for Real-Time Prediction of Bus Trajectories
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The uncertainty associated with public transport services can be partially counteracted by developing real-time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical data. The contribution of each predictor as well as values of respective parameters is estimated by minimizing the prediction error using a linear regression based heuristic. The hybrid method was applied to five bus lines in Stockholm, Sweden and Brisbane, Australia. The results indicate that the hybrid method consistently outperforms the timetable and delay conservation prediction method for different line layouts, passenger demands and operation practices. Model validation confirms model transferability and real-time applicability. Generating more accurate predictions can help service users adjust their travel plans and service providers to deploy proactive management and control strategies to mitigate the negative effects of service disturbances.

National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-177486 (URN)
Note

QS 2015

Available from: 2015-11-20 Created: 2015-11-20 Last updated: 2016-02-21Bibliographically approved

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Fadaei Oshyani, Masoud

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