Synergistic Interactions of Dynamic Ridesharing and Battery Electric Vehicles Land Use, Transit, and Auto Pricing Policies

It is widely recognized that new vehicle and fuel technology is necessary, but not sufficient, to meet deep greenhouse gas (GHG) reductions goals for both the U.S. and the state of California. Demand management strategies (such as land use, transit, and auto pricing) are also needed to reduce passenger vehicle miles traveled (VMT) and related GHG emissions. In this study, the authors explore how demand management strategies may be combined with new vehicle technology (battery electric vehicles or BEVs) and services (dynamic ridesharing) to enhance VMT and GHG reductions. Owning a BEV or using a dynamic ridesharing service may be more feasible when distances to destinations are made shorter and alternative modes of travel are provided by demand management strategies. To examine potential markets, we use the San Francisco Bay Area activity based travel demand model to simulate business-as-usual, transit oriented development, and auto pricing policies with and without high, medium, and low dynamic ridesharing participation rates and BEV daily driving distance ranges. The results of this study suggest that dynamic ridesharing has the potential to significantly reduce VMT and related GHG emissions, which may be greater than land use and transit policies typically included in Sustainable Community Strategies (under California Senate Bill 375), if travelers are willing pay with both time and money to use the dynamic ridesharing system. However, in general, large synergistic effects between ridesharing and transit oriented development or auto pricing policies were not found in this study. The results of the BEV simulations suggest that TODs may increase the market for BEVs by less than 1% in the Bay Area and that auto pricing policies may increase the market by as much as 7%. However, it is possible that larger changes are possible over time in faster growing regions where development is currently at low density levels (for example, the Central Valley in California). The VMT Fee scenarios show larger increases in the potential market for BEV (as much as 7%). Future research should explore the factors associated with higher dynamic ridesharing and BEV use including individual attributes, characteristics of tours and trips, and time and cost benefits. In addition, the travel effects of dynamic ridesharing systems should be simulated explicitly, including auto ownership, mode choice, destination, and extra VMT to pick up a passenger

Testing demand responsive shared transport services via agent-based simulations

Demand Responsive Shared Transport DRST services take advantage of Information and Communication Technologies ICT, to provide on demand transport services booking in real time a ride on a shared vehicle. In this paper, an agent-based model ABM is presented to test different the feasibility of different service configurations in a real context. First results show the impact of route choice strategy on the system performance

Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations

In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns

Towards a Testbed for Dynamic Vehicle Routing Algorithms

Since modern transport services are becoming more flexible, demand-responsive, and energy/cost efficient, there is a growing demand for large-scale microscopic simulation platforms in order to test sophisticated routing algorithms. Such platforms have to simulate in detail, not only the dynamically changing demand and supply of the relevant service, but also traffic flow and other relevant transport services. This paper presents the DVRP extension to the open-source MATSim simulator. The extension is designed to be highly general and customizable to simulate a wide range of dynamic rich vehicle routing problems. The extension allows plugging in of various algorithms that are responsible for continuous re-optimisation of routes in response to changes in the system. The DVRP extension has been used in many research and commercial projects dealing with simulation of electric and autonomous taxis, demand-responsive transport, personal rapid transport, free-floating car sharing and parking search