Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion

Abstract

Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.

Bi Objective Peer-to-Peer Ridesharing Model for Balancing Passengers Time and Costs

“…Kornhauser [13] was the first researcher who studied ride-sharing systems to help fuel management in the United States and implement a new urban transportation system. This study showed that the use of ride-sharing systems noticeably reduced the energy consumption of taxis, and the work attracted much attention from researchers for different reasons, including reducing the waste of energy [14] relieving traffic congestion [10,15] adjusting prices dynamically [16], and increasing customer satisfaction [17]. Other research regarding ridesharing can be found in [18,19].…”

“…Some works such as [9,10] assume that drivers have a fixed and single direction of travel with specific origins and destinations, and in some other cases, detours might occur to pick up and drop off travelers outside the main route, which cannot be a practical assumption for the actual transportation network pattern. To provide more flexibility to ride-sharing services, we assume in this paper that multiple heterogeneous vehicles can operate simultaneously in an area with different travel routes.…”

“…Moreover, according to this constraint, not all vehicles need to be dispatched. Constraint (10) states that each vehicle cannot travel to more than one destination point from all passenger nodes. Constraint (11) ensures that each vehicle can only travel to a maximum of one of the destination nodes and one other destination point from each destination point.…”

Sustainable Low-Carbon Layout of Land around Rail Transit Stations Based on Multi-Modal Spatial Data

“…It was found that public transport accessibility, the spatial relationship between employment and housing, the urban spatial form and land-use planning are the main factors affecting commuter carbon emissions [29][30][31][32][33]. Jiang Yang et al combined the energy consumption factors of various modes of transport with the commuting patterns of residents, calculated transport energy consumption by distance travelled, and determined the influence of urban neighborhood form on residents' travel energy consumption by analyzing the morphological characteristics of different neighborhoods [34]. Many scholars have found that the accessibility of public transport, the spatial relationship between jobs and housing, the urban spatial form and the land-mixing degree are the main factors affecting the carbon emissions of commuting transport [30,35].…”