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.

Disruptions in public transport networks (PTNs) often lead to partial capacity reductions rather than complete closures. This study aims to move beyond the vulnerability analysis of complete failures by investigating the impacts of a range of capacity reductions on PTN performance. The relation between network performance and the degradation of line or link capacities is investigated by establishing a vulnerability curve and related metrics. The analysis framework is applied to a full-scan analysis of planned temporary line-level capacity reductions and an analysis of unplanned link-level capacity reductions on the most central segments in the multi-modal rapid PTN of Stockholm, Sweden. The impacts of capacity reductions are assessed using a non-equilibrium dynamic public transport operations and assignment model. The nonlinear properties of on-board crowding, denied boarding, network effects and route choice result in non-trivial, generally convex, relations which carry implications on disruption planning and real-time management.

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 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.

The paper proposes a methodology for the identification of workstations in earthwork operations based on GPS traces from construction vehicles. The model incorporates relevant information extracted from the GPS data to infer locations of different workstations as probability distributions over the environment. Monitoring of workstation locations may support map inference for generating and continuously updating the layout and road network topology of the construction environment. A case study is conducted at a complex earthwork site in Sweden. The workstation identification methodology is used to infer the locations of loading stations based on vehicle speeds and interactions between vehicles, and the locations of dumping stations based on vehicle turning patterns. The results show that the proposed method is able to identify workstations in the earthwork environment efficiently and in sufficient detail.

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.

Urban traffic simulation models could benefit significantly from new validation methods with potential to reduce the time-consuming calibration and validation work needed before application of the model to evaluate city infrastructure or policy implementations. Current practice is to validate simulation models locally through comparison with point flow measurements and travel times on some important routes. However, for many applications, the level of congestion in an entire area is important. During the last decade, several studies have found empirical evidence of a relation between flow and density on city district level, the existence of a so-called macroscopic fundamental diagram (MFD). This paper shows how the MFD can be used to validate results from a traffic simulation model for a city district. Furthermore, the paper shows empirical results for Stockholm, Sweden.

Urban traffic simulation models could benefit significantly from new validation methods with potential to reduce the time-consuming calibration and validation work needed before application of the model to evaluate city infrastructure or policy implementations. Current practice is to validate simulation models locally through comparison with point flow measurements and travel times on some important routes. However, for many applications, the level of congestion in an entire area is important. During the last decade, several studies have found empirical evidence of a relation between flow and density on city district level, the existence of a so-called macroscopic fundamental diagram (MFD). This paper shows how the MFD can be used to validate results from a traffic simulation model for a city district. Furthermore, the paper shows empirical results for Stockholm, Sweden.

Holding has been extensively investigated as a strategy to mitigate the inherently stochastic nature of public transport operations. Holding focuses on either regulating vehicle headways using a rule-based approach or minimizing passenger travel cost by employing optimization models. This paper introduces a holding decision rule that explicitly addresses passenger travel cost. The decision to hold relies on the passenger demand distribution along the line. The passenger cost holding rule is tested using simulation for a high frequency bus line in Stockholm, Sweden and is compared with a no-control scheme and the currently used headway-based strategy. The results indicate that the new decision rule results in relatively minor reductions of passenger cost compared to the currently adopted strategy, and that it allocates the greatest share of holding time at the beginning of the route.