This study uses mobile phone data to understand mobility patterns in a country, with limited mobility data, in order to give advice about decisions on how to design the national and regional road network. Our method consists of three parts: (1) filtering mobile phone traces to derive mobility patterns, (2) building an adapted formulation of the gravity-based trip distribution model, which considers telecommunication intensity (i.e., aggregate number of calls and text messages) and travel time as input to forecast the influence of road improvements on country-wide mobility, and (3) optimizing the road network investment based on the adapted trip distribution model by using a local search algorithm. The method was applied to the case study country of Senegal. The mobile phone data was transformed to support informed decisions on road network development in that country given different objectives, namely accessibility and equity. We believe that the methodology is valuable and reproducible to other countries where traditional mobility data is scarce but mobile phone data is available to transport planners.@en

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip information from SCD to model location choice of after-work activities. We newly defined the metrics of travel impedance in this case. Moreover, since socio-demographic information is missing in such anonymous data, we used observable proxy indicators, including commuting distance and the characteristics of one's home and workplace stations, to capture some interpersonal heterogeneity. Such heterogeneity is expected to distinguish the population and better explain the difference of their location choice behaviour. The approach was applied to metro travellers in the city of Shanghai, China. As a result, the model performs well in explaining the choices. Our new metrics of travel impedance to access an after-work activity result in a better model fit than the existing metrics and add additional interpretability to the results. Moreover, the proxy variables distinguishing the population seem to influence the choice behaviour and thus improve the model performance.

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This chapter deals with the Dutch smart card system, the so-called OV-Chipkaart and illustrates a potential application of its data. This chapter explores options for using this anonymous smart card data for evaluation, analysis and performing simple what-if analyses by using transport planning software. The objective is to process the data in such a way that it enhances evaluation and prediction of ridership (patterns). This helps to improve network and time-table design. The main contribution of this research is to introduce smart card as a data source into existing methods to come to a new ridership prediction approach. Our approach takes comfort into account, since it is a relevant quality indicator, which is often neglected. We show that the effect of a frequency increase in a congested public transport line in terms of additional passengers becomes significantly larger when comfort effects are included. Our approach was applied as a case study to the tram network in The Hague. The approach proved very valuable to gain insights on the effect of changes in the public transport supply. @en

For decades congestion levels around the world are rising. To properly incorporate the effects of congestion into strategic transport models, a shift from static capacity restrained towards capacity constrained and dynamic traffic assignment models has occurred. In this paper we focus on quasi dynamic assignment models (more specific: static-capacity and storage constrained models by the definitions in Bliemer et al (2015)). These models explicitly capture the flow metering and spillback effects of congestion, but assume stationary demand during a single time period (e.g. a whole peak hour) and are therefore more scalable and mathematically tractable, both important properties for strategic transport models. Although computational capabilities of current hardware allow for large scale application of such models, the incorporation of capacity constraints causes route cost functions to be much more sensitive and to be inseparable over space (the latter occurs when routes share bottleneck nodes). Furthermore, the incorporation of storage constraints further increases inseparability (which occurs when queues spill back onto upstream links) and causes cost functions to become implicit. As such quasi dynamic models do not fully contain the favorable mathematical properties that are exploited in many algorithms to solve their capacity restrained counterparts and in fact do not necessarily comply with the requirements for existence and/or uniqueness of the user equilibrium (theorems 1.4 and 1.8 in Nagurney (1993)). Although in reality these unfavorable properties exist, a substantial body of research suggests that their (spatial) occurrence is limited and as such “…have minimal practical temporal and spatial consequences…” (Peeta and Zilliaskopoulos (2001)). However, several large scale applications using the quasi dynamic assignment model STAQ (first described in Brederode et al (2010)) have shown that especially the addition of storage constraints causes poor or non-convergence in real world applications. Further investigations in this paper will show that also the capacity constraints on their own can cause serious convergence issues. Contributions in this paper are (i) to give an overview of methods in literature and logical extensions to those methods that could improve convergence of quasi dynamic assignment models, (ii) to reveal and illustrate mechanisms that cause the convergence issues using examples on theoretical networks and (iii) to investigate to what extent enhancements to existing algorithms can be used to (partly) get around the convergence issues encountered. Ultimately, this research should lead to a method that generically solves quasi dynamic assignment models. @en

Reliable and accurate short-term traffic state prediction can improve the performance of real-time traffic management systems significantly. Using this short-time prediction based on current measurements delivered by advanced surveillance systems will support decision-making processes on various control strategies and enhance the performance of the overall network. By taking proactive action deploying traffic management measures, congestion may be prevented or its effects limited. An approach of short-term traffic state prediction is presented and implemented in a real life case for the city of Assen in the Netherlands. This prediction is based on connecting online traffic measurements with a real time traffic model using the macroscopic dynamic traffic assignment model StreamLine in a rolling horizon implementation. Different monitoring data sources consisting of both fixed-point and floating car data are used. The advantage of the rolling horizon approach is that no warming-up period is needed for the dynamic traffic assignment taking less computation time while keeping results consistent. Further, the current traffic state estimation is done by combining model estimates of previous predictions and current measurements. The results of predictions made in the real life case are presented as well as several tested methods for improving the current state estimations showing promising results.@en

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