Requirements for traffic assignment models for strategic transport planning: A critical assessment

Transport planning models are used all over the world to assist in the decision making regarding investments in infrastructure and transport services. Traffic assignment is one of the key components of transport models, which relate travel demand to infrastructure supply, by simulating (future) route choices and network conditions, resulting in traffic flows, congestion, travel times, and emissions. Cost benefit analyses rely on outcomes of such models, and since very large monetary investments are at stake, these outcomes should be as accurate and reliable as possible. However, the vast majority of strategic transport models still use traditional static traffic assignment procedures with travel time functions in which traffic flow can exceed capacity, delays are predicted in the wrong locations, and intersections are not properly handled. On the other hand, microscopic dynamic traffic simulation models can simulate traffic very realistically, but are not able to deal with very large networks and may not have the capability of providing robust results for scenario analysis. In this paper we discuss and identify the important characteristics of traffic assignment models for transport planning. We propose a modelling framework in which the traffic assignment model exhibits a good balance between traffic flow realism, robustness, consistency, accountability, and ease of use. Furthermore, case studies on several large networks of Dutch and Australian cities will be presented

Short-Term Prediction of Ridership on Public Transport with Smart Card Data

Public transport operators are collecting massive amounts of data from smart card systems. In the Netherlands, every passenger checks in and checks out; this system creates detailed records of demand patterns. In buses and trams, users check in and check out in the vehicle; this factor provides good information on route choice. Options for analyzing smart card data and performing what-if analyses with transport planning software were explored. On the basis of big data, this new generation of transport demand models added to the existing range of transport demand models and approaches. The goal was to provide public transport operators with a simple (easy-to-build) model to perform what-if analyses. The data were converted to passengers per line and an origin–destination matrix between stops. This matrix was assigned to the network to reproduce the measured passenger flows, and then what-if analysis became possible. With fixed demand, line changes could be investigated. With the introduction of an elastic demand model, changes in the level of service realistically affected passenger numbers. This method was applied to a case study in The Hague, Netherlands. Smart card data were imported into a transport model and connected with the network. The tool proved to be valuable to operators, who gained insights into the effects of small changes

Unreliability effects in public transport modelling.

Nowadays, transport demand models do not explicitly evaluate the impacts of service reliability of transit. Service reliability of transit systems is adversely experienced by users, as it causes additional travel time and unsecure arrival times. Because of this, travellers are likely to perceive a higher utility from more reliable transport systems. In order to mimic and measure the impacts of service reliability on a transit demand model a three-step approach is proposed using automated vehicle location and smart card data. The approach consists of determining the probabilistic distribution of transit trip times, defining demand patterns and estimating the average impacts of unreliability per passenger. This approach was successfully tested on the model of the city of Utrecht in The Netherlands. By adding service reliability as a variable parameter of transit systems the results of the demand model improved, showing that the absolute difference between the observed and the estimated demand decreased by 18%. In addition, the proposed approach allows measuring the effects of expected changes in level of service reliability on traveller behaviour. Finally, we identify future research topics required to improve the estimation of those effects

Static Traffic Assignment with Queuing: model properties and applications

This paper describes the road traffic assignment model Static Traffic Assignment with Queuing (STAQ) that was developed for situations where both static (STA) and dynamic (DTA) traffic assignment models are insufficient: strategic applications on large-scale congested networks. The paper demonstrates how the model overcomes shortcomings in STA and DTA modelling approaches in the strategic context by describing its concept, methodology and solution algorithm as well as by presenting model applications on (small) theoretical and (large) real-life networks. The STAQ model captures flow metering and spillback effects of bottlenecks like in DTA models, while its input and computational requirements are only slightly higher than those of STA models. It does so in a very tractable fashion, and acquires high-precision user equilibria (relative gap < 1E-04) on large-scale networks. In light of its accuracy, robustness and accountability, the STAQ model is discussed as a viable alternative to STA and DTA modelling approaches

Static Traffic Assignment with Queuing: Model properties and applications

This paper describes the road traffic assignment model Static Traffic Assignment with Queuing (STAQ) that was developed for situations where both static (STA) and dynamic (DTA) traffic assignment models are insufficient: strategic applications on large-scale congested networks. The paper demonstrates how the model overcomes shortcomings in STA and DTA modelling approaches in the strategic context by describing its concept, methodology and solution algorithm as well as by presenting model applications on (small) theoretical and (large) real-life networks. The STAQ model captures flow metering and spillback effects of bottlenecks like in DTA models, while its input and computational requirements are only slightly higher than those of STA models. It does so in a very tractable fashion, and acquires high-precision user equilibria (relative gap &lt; 1E-04) on large-scale networks. In light of its accuracy, robustness and accountability, the STAQ model is discussed as a viable alternative to STA and DTA modelling approaches.Transport and PlanningTransport and Plannin