Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

Network Fundamental Diagram Based Routing of Vehicle Fleets in Dynamic Traffic Simulations

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

Constructing a Network Fundamental Diagram: Synthetic Origin–Destination Approach

The network fundamental diagram (NFD) is increasingly used in traffic monitoring and control. One obstacle to a wider application of NFDs in network control is the difficulty of obtaining data from all vehicles traveling in the network to construct an accurate NFD. One solution is to estimate the NFD using data from only a fraction of vehicles (i.e., probe vehicles), where the probe vehicle market penetration rate (MPR) needs to be estimated. A previous study conducted by the authors demonstrated that a distance or time-weighted harmonic mean was needed to estimate the flow- and density-based MPRs, respectively, using a pairing k-mean clustering approach. This paper proposes another approach that utilizes probe vehicle and observed link volume data to estimate the MPR. A heuristic model is proposed to identify the optimum locations from which to collect link traffic volume data for use in the MPR estimation. The estimated MPR can then be used to construct the NFD. Results show that these models can accurately estimate the NFD with limited probe vehicle and link traffic volume data. Accordingly, the models can be used in the field to estimate the NFD using readily available loop detector and probe vehicle data. The ideal locations for traffic volume data collection can also be proactively chosen to generate optimum estimation results. As the models proposed here show no significant gains with an increased magnitude of collected data after a certain threshold, they will be helpful, particularly when large-scale data collection is not affordable or realistic.