Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks

Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph's temporal-spatial features, and a sequential convolutional network for multi-source data (e.g., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.

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

Ride-sharing services are one of the top growing sustainable transportation trends led by mobility-as-a-service companies. Ridesharing is a system that provides the ability to share vehicles on similar routes for passengers with similar or nearby destinations on short notice, leading to decreased costs for travelers. At the same time, though, it takes longer to get from place to place, increasing travel time. Therefore, a fundamental challenge for mobility service providers should be finding a balance between cost and travel time. This paper develops an integer bi-objective optimization model that integrates vehicle assignment, vehicle routing, and passenger assignment to find a non-dominated solution based on cost and time. The model allows a vehicle to be used multiple times by different passengers. The first objective seeks to minimize the total cost, including the fixed cost, defined as the supply cost per vehicle, and the operating cost, which is a function of the distance traveled. The second objective is to minimize the time it takes passengers to reach their destination. This is measured by how long it takes each vehicle to reach the passenger’s point of origin and how long it takes to get to the destination. The proposed model is solved using the AUGMECON method and the NSGA II algorithm. A real case study from Sioux Falls is presented to validate the applicability of the proposed model. This study shows that ridesharing helps passengers save money using mobility services without significant change in travel time.