相关论文: Ride-hailing Impacts on Transit Ridership: Chicago…
The on-demand ride-hailing industry has experienced rapid growth, transforming transportation norms worldwide. Despite improvements in efficiency over traditional taxi services, significant challenges remain, including drivers' strategic…
Ride-sharing can reduce traffic congestion and thus reduce gas emissions and save travel time. However, transportation system with ride-sharing is currently inefficient due to low occupancy rate, high travel demand and some other factors.…
Even before the start of the COVID-19 pandemic, bus ridership in the United States had attained its lowest level since 1973. If transit agencies hope to reverse this trend, they must understand how their service allocation policies affect…
Ubiquitous mobile computing have enabled ride-hailing services to collect vast amounts of behavioral data of riders and drivers and optimize supply and demand matching in real time. While these mobility service providers have some degree of…
This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is…
Rideshare is one way to share mobility in transportation without increasing traffic demand, where travel mobility and usage of vehicle capacity can be improved. However, current literature on rideshare has allowed only one-modal trips and…
Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a…
Ridesharing services have revolutionized personal mobility, offering convenient on-demand transportation anytime. While early proponents of ridesharing suggested that these services would reduce the overall carbon emissions of the…
This case-study aims at a comparison of the service quality of time-tabled buses as compared to on-demand ridepooling cabs in the late evening hours in the city of Wuppertal, Germany. To evaluate the service quality of ridepooling as…
Integrating ridesharing matching explicitly into multimodal traffic models is crucial for accurately assessing the impacts of multimodal transport (MT) on urban economic and environmental aspects. This paper integrates an optimal…
Understanding human mobility patterns is important in applications as diverse as urban planning, public health, and political organizing. One rich source of data on human mobility is taxi ride data. Using the city of Chicago as a case…
Ride-pooling has become an important service option offered by ride-hailing platforms as it serves multiple trip requests in a single ride. By leveraging customer data, connected vehicles, and efficient assignment algorithms, ride-pooling…
On-demand mobility platforms play an increasingly important role in urban mobility systems. Impacts are still debated, as these platforms supply personalized and optimized services, while also contributing to existing sustainability…
In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet…
With the climate emergency and growing challenges ranging from pollution to congestion, ride-pooling (rp) has been floated as a potential solution for less congested, low-carbon and more space-efficient urban transportation. However, it is…
The purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in Minneapolis - St. Paul Metropolitan Area in Minnesota. We obtained the number of trips to and from each of the…
This paper focuses on modeling ride requests and their variations over location and time, based on analyzing extensive real-world data from a ride-sharing service. We introduce a graph model that captures the spatial and temporal…
Passenger waiting time prediction plays a critical role in enhancing both ridesharing user experience and platform efficiency. While most existing research focuses on post-request waiting time prediction with knowing the matched driver…
Existing studies have extensively used spatiotemporal data to discover the mobility patterns of various types of travellers. Smart card data (SCD) collected by the automated fare collection systems can reflect a general view of the mobility…
Recent advances in communication technologies and automated vehicles have opened doors for alternative mobility systems (taxis, carpool, demand-responsive services, peer-to-peer ridesharing, and car sharing, shared autonomous…