Demand-Responsive Transport for Students in Rural Areas: A Case Study in Vulkaneifel, Germany

Lu, Chengqi; Maciejewski, Michał; Wu, Hao; Nagel, Kai

doi:10.2139/ssrn.4181254

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The algorithm minimizes the total distance driven by all buses while distributing users over all buses. The algorithm includes rebalancing [8,[26][27][28], i.e. sending back idling buses towards a central location to avoid that empty buses get stuck in regions of low demand (details in supplementary note 1.C.4).…”

Section: Methodsmentioning

confidence: 99%

Taming travel time fluctuations through adaptive stop pooling

Lotze,

Marszal,

Schröder

et al. 2024

J. Phys. Complex.

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Ride sharing services combine trips of multiple users in the same vehicle and may provide more sustainable transport than private cars. As mobility demand varies during the day, the travel times experienced by passengers may substantially vary as well, making the service quality unreliable.We show through model simulations that such travel time fluctuations may be drastically reduced by stop pooling. Having users walk to meet at joint locations for pick-up or drop-off allows buses to travel more direct routes by avoiding frequent door-to-door detours, especially during high demand.We in particular propose adaptive stop pooling by adjusting the maximum walking distance to the temporally and spatially varying demand.The results highlight that adaptive stop pooling may substantially reduce travel time fluctuations while even improving the average travel time of ride sharing services, especially for high demand. Such quality improvements may in turn increase the acceptance and adoption of ride sharing services.

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Section: Methodsmentioning

confidence: 99%

Taming travel time fluctuations through adaptive stop pooling

Lotze,

Marszal,

Schröder

et al. 2024

J. Phys. Complex.

View full text Add to dashboard Cite

show abstract

“…In earlier studies, researchers [10][11][12] analyzed two distinct types of DRT transportation services, namely door-to-door (d2d) and stop-based (sB) [10][11][12]. Door-to-door service involves picking up and dropping off passengers at their preferred location, thus providing a highly convenient transportation service similar to a taxi.…”

Section: Introductionmentioning

confidence: 99%

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

Salman

Sisiopiku

Khalil

et al. 2023

Future Transportation

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Transportation Network Companies (TNCs) use online-enabled apps to provide on-demand transportation services. TNCs facilitate travelers to connect with drivers that can offer them rides for compensation using driver-owned vehicles. The ride requests can be for (a) individual or (b) shared rides. The latter, also known as ride-pooling services, accommodates requests of unrelated parties with origins and destinations along the same route who agree to share the same vehicle, usually at a discounted fare. Uber and Lyft offer ride-pooling services in select markets. Compared to individual ride requests, ride-pooling services hold better promise toward easing urban congestion by reducing the number of automobiles on the road. However, their impact on traffic operations is still not fully understood. Using Birmingham, AL as a case study, this research evaluated the impact that ride-pooling services have on traffic operations using a Multi-Agent Transport Simulation (MATSim) model of the Birmingham metro area. Scenarios were developed to simulate baseline conditions (no TNC service) and ride-pooling availability with two types of ride-pooling services, namely door-to-door (d2d) and stop-based (sB) service and three fleet sizes (200, 400, and 800 vehicles). The results indicate that when TNC vehicles are added to the network, the Vehicle Kilometers Traveled (VKT) decrease by up to 5.78% for the door-to-door (d2d) service, and up to 2.71% for stop-based (sB) services, as compared to the baseline scenario (no TNC service). The findings also suggest that an increase in the size of the ride-pooling fleet results in a rise in total ride-pooling service VKT, network-wide total VKT, and detour distance. However, increasing the size of the ride-pooling fleet also results in a decrease in the ride request rejection rates, thus benefiting the customers and decreasing the vehicle empty ratio which, in turn, benefits the TNC drivers. The results further suggest that a fleet of 200 ride-pooling vehicles can meet the current demand for service in the Birmingham region at all times, thus it is the optimal ride-pooling TNC fleet size for a medium-sized city such as Birmingham.

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Optimization of demand-responsive transport: The rolling horizon approach

Maciejewski

et al. 2023

Procedia Computer Science

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Demand-Responsive Transport for Students in Rural Areas: A Case Study in Vulkaneifel, Germany

Cited by 3 publications

References 17 publications

Taming travel time fluctuations through adaptive stop pooling

Taming travel time fluctuations through adaptive stop pooling

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

Optimization of demand-responsive transport: The rolling horizon approach

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