Agent-Based Simulation Approach to Determine Safety Impacts of Demand-Responsive Transport (DRT) in Wayne County, Michigan

Abstract

Road safety is one of the major concerns in transportation system management. Safety performance analyses usually assess crash frequencies and the impacts of countermeasures on the number of crashes. However, the advent of new mobility solutions makes safety evaluation more challenging; data tend to be sparse, and the impacts of such services on demand and the performance of the broader network is not completely understood. This paper attempts to fill this gap by creating a novel method to estimate the safety impacts of Demand-Responsive Transport (DRT) services. Using an agent-based mesoscopic model in the Multi-Agent Transport Simulation (MATSim) Toolkit, we simulate DRT as a new mobility solution in Wayne County, Michigan, and obtain spatial and temporal distributions of traffic volume and operating speed to predict the frequency and severity of crashes. We tested for different DRT service designs, such as fleet size, detour tolerance, and initial placement of the fleet. Our findings indicate that introducing a DRT service on its own increases vehicle kilometers traveled (VKT) by 22% and consequently the number of crashes by 17%. However, this impact could be ameliorated by increasing the detour tolerance, which can significantly increase the number of shared rides and decreases the crash frequency (10.8%) and crash severity as a result of a lower VKT (15%). The output of the proposed framework serves as a starting point of a safety performance evaluation using an agent-based simulation approach to achieve a safe and successful implementation of shared mobility services in large-scale urbanized areas.

Operational Impacts of On-Demand Ride-Pooling Service Options in Birmingham, AL

“…A further 20% reduction was observed when the fleet size increased from 400 to 800 vehicles. These findings are consistent with results from a previous study [18], which reported that increasing the DRT fleet size from 150 to 250 vehicles resulted in a 36% decrease in rejection rates and a 28% reduction in the rejection rate when the DRT fleet size was increased from 250 to 350 vehicles [18]. It should be noted that the Birmingham simulation study assumed a maximum acceptable wait time of 5 min.…”

“…For example, in their study of Demand Responsive Transit (DRT), Kagho et al [17] found that the introduction of such a service was likely to increase overall VKT slightly in Wayne County, Michigan. Another study conducted in the same area [18] confirmed this finding and concluded that the introduction of DRT can increase the VKT by 22%. Furthermore, a study by Wu and MacKenzie [19] used 2017 US National Household Travel Survey data to examine the heterogeneous VKT effects on ride-sharing across population groups and reported an estimated net increase of 12.55 million VKT per day in the US due to ridesharing, compared to a case where all NHTS 2017 respondents are considered to be non-users of ride-sharing services.…”

“…To date, most studies on the impact of ride-pooling services have been conducted in cities/counties larger in population size than Birmingham metro area, which has a population of 0.89 million [29]. For instance, studies have been carried out in: Hangzhou, China [13] (8.24 million population [30]); Haikou City, China [14] (2.02 million population [30]); Berlin, Germany [15] (3.57 million population [30]); Santiago, Chile [16] (6.9 million population [30]); and Wayne County, Michigan [17,18] (1.77 million population [31]). Since the population size affects transportation demand, further research is needed to investigate how ride-pooling services affect the operational efficiency of the transportation system in medium-sized cities (population between 350,000-999,999).…”