Abstract

Accessibility is a concept which either looks at how easy it is to reach a certain location from many other locations, or how easy it is to reach other locations or opportunities from a given starting point. This chapter looks at the latter, discussing that this is a quantity that can be defined separately for every point (x,y) in space, rather than treating accessibility as uniform within, say, zones. As a result, accessibility can also be seen as a continuous field A(x,y) in the two-dimensional environment. The chapter then continues to discuss how A(x,y) can be efficiently computed for regional scenarios. The approach combines interpolation of values computed on a grid with fast shortest-path tree computations and information caching for repeated sub-computations of the same quantities, using the econometric logsum term as an example of a possible indicator of accessibility. A Zurich scenario needs about two minutes of computing time on a regular desktop computer in order to compute A(x,y) at a resolution of 100 m x 100 m. As a sensitivity study, workplace accessibility maps are given for free speed car, congested car, bicycle and walking. One can for example observe that accessibility by bicycle is similar to congested car accessibility within the urban area, while it is worse outside and considerably worse when compared to free speed car transport. Similarly, walking accessibility is similar to bicycle and congested car transport in the innermost urban core, but considerably worse everywhere else.

Abstract

We investigate setting up a traffic simulation scenario only from a high spatial resolution set of mobile phone trajectories and an OpenStreetMap road network model. Mixed road traffic is modelled as the result of a choice for each user between a simulated congested car mode and a non-congested alternative mode parameterized by its speed.

Abstract

Over the last two decades, electrification has gained importance as a means to decarbonise the transport sector. As the number of Electric Vehicles (EVs)increases, it is important to consider broader system aspects as well, especially when deciding the type, coverage, size and location of the charging infrastructure required. In this article, a Multi-Agent model depicting long distance transport in Sweden is proposed, allowing to simulate different scenarios and enabling a more detailed analysis of the interaction between these vehicles and the charging infrastructure.

Abstract

Recently, ridesharing services have grown rapidly. In future, fleets of shared and pooled autonomous vehicles may transform urban mobility. In this paper, we introduce an approach to dynamically simulate these services within a full-stack transport simulation using an insertion-based algorithm. In a first test case, using a taxi data set from Berlin, the potential for shared rides is evaluated using a fleet of vehicles with a capacity between two and four ride requests. The simulation suggests that the overall vehicle kilometers traveled may be reduced by 15-20%, while travel time increases can be kept at a relatively low level of less than three minutes per person. Additionally, the simulation results suggest in which areas of the city it may be the most rewarding to offer shared services.

Abstract

Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.