Dynamic Grid-Based Spatial Density Visualization and Rail Transit Station Prediction

The urban rail transit stations are an important part of an urban transit system. Scientific and reasonable location of rail transit station can greatly alleviate traffic pressure. The number of people in the surrounding area of a rail transit station is an important factor for site selection. However, it is difficult to obtain the spatial distribution of population, which brings great difficulties in terms of site selection. Due to the large-scale popularization of AP (Access Point) in China, the spatial distribution of AP is used instead of population distribution to assist site selection. Therefore, a density visualization method based on a dynamic grid is proposed, which can help decision-makers intuitively see the AP density of the uncovered grid of rail transit stations, and then cluster the AP density of the uncovered area to predict the location of new rail transit stations. The validity of the proposed method is demonstrated by using the AP dataset and rail transit data of Beijing in 2013. The results show that our method has high accuracy in predicting the location of rail transit stations. It can provide data support for urban traffic development and management.

Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption

Problem definition: Because of a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel by public transit instead of driving personal vehicles, municipal governments must address the “last-mile” problem by providing convenient and affordable transportation between a commuter’s home and a transit station. This challenge raises an important question: Is there a cost-effective mechanism that can improve public transit adoption by solving the last-mile problem? Academic/practical relevance: In this paper, we present and analyze two incentive mechanisms for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a “mixed mode,” which involves combining public transit with hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. We examine the implications of both mechanisms on relevant stakeholders. These two mechanisms are especially relevant because several jurisdictions in the United States have begun piloting incentive programs, in which commuters receive subsidies for ride-hailing trips that begin or end at a transit station. Methodology: We present a game-theoretic model to capture the strategic interactions among five self-interested stakeholders (commuters, public transit agency, ride-hailing platform, municipal government, and local private enterprises). Results: By examining equilibrium outcomes, we obtain three key findings. First, we characterize how the optimal interventions associated with the direct or the indirect mechanism depend on: (a) the coverage level of the public transit network; (b) the public transit adoption target; and (c) the relative strength of commuter preferences between driving and taking public transit. Second, we show that the direct mechanism cannot be budget-neutral without undermining commuter welfare. However, when the public transit adoption target is not too aggressive, we find that the indirect mechanism can increase both commuter welfare and sales to the private-sector partner while remaining budget-neutral. Finally, we show that, although the indirect mechanism restricts the scope of government intervention (by eliminating the congestion fee), it can dominate the direct mechanism by leaving all stakeholders better off, especially when the adoption target is modest. Managerial implications: Our findings offer cost-effective prescriptions for improving urban mobility and public transit ridership.