In developing countries, the Bus Rapid Transit System (BRTS) has recently taken on a significant role as a means of public transportation in urban areas. In BRTS, women’s travel patterns differ from men’s due to differences in trip purposes and accessibility to facilities; furthermore, they encounter considerable challenges concerning safety, comfort, and convenience during their journey. Therefore, this study aims to develop a model for predicting the quality of service (QoS) of the BRTS based on the perception of female passengers. A convenience sample of 530 responses from BRTS female passengers was collected through a structured questionnaire survey in Indore, India. The study employed the ordered probit (OP), support vector machines (SVM), and artificial neural networks (ANN) approaches to assess the perceptions of women’s passenger service quality on BRTS. The perception of female passengers was analysed based on socioeconomic factors, service quality attributes, trip characteristics, and operational features. The model outcomes demonstrated that ANN with a tanh activation function had superior results with an accuracy level of 66.13
This study develops a data-driven framework to estimate spatial–temporal transit demand patterns of older adults in Winnipeg, a mid-sized, single-mode bus network, to inform Demand-Responsive Transit (DRT) zoning strategies under data-constrained conditions. Recognizing the uneven spatial distribution of older adults and seasonal variability in travel demand, the framework generates monthly Origin–Destination (OD) matrices by combining aggregate boarding data with symmetry-based alighting estimation, calibrated using a weighted averaging approach. The resulting spatial and temporal analyses reveal pronounced seasonal variation in trip distance sensitivity, with longer-distance trips prevailing during low-sensitivity periods and more localized travel dominating during high-sensitivity periods. These patterns highlight the importance of adaptive DRT zoning, suggesting radial layouts with smaller, denser zones during warmer months and larger zones during colder periods. Using widely available aggregate data, the proposed approach provides a scalable and data-efficient tool for supporting early-stage, elderly-oriented DRT zoning decisions, particularly in smaller transit systems where detailed behavioral data are unavailable.
We address an operational planning problem faced by public transit companies that operate a fleet of electric buses (EBs) out of an indoor parking (depot). Given a set of pre-computed sequences of trips (STs) that start and end at the depot, a set of identical EBs, a parking composed of identical lanes operated using a first-in, first-out (FIFO) policy, and a set of chargers, the Electric Bus Assignment Problem with Parking Constraints (eBAP-PC) consists in assigning an EB to each ST, assigning a lane to each EB returning to the depot after a ST, and scheduling its recharge such that the battery of each EB is never fully depleted. Feasible solutions must also ensure that the charging capacity is never exceeded and the EBs simultaneously parked in the same lane respect the lane capacity and the FIFO policy. Cast as a feasibility problem, we first model the eBAP-PC as a mixed-integer linear program that exploits a decomposition of the problem into three interrelated subproblems. We then introduce three techniques to accelerate its solution process. Finally, we report the results of three computational experiments conducted on instances derived from two real-world datasets. First, we assess the effectiveness of the acceleration strategies. Second, we perform a sensitivity analysis on some instance parameter values. Last, we benchmark our solution method against two constraint programming methods from the literature.
Fare evasion remains a persistent challenge for public transit systems, yet traditional methods of estimating its impact such as manual inspections and surveys are limited in coverage and subject to sampling bias. This study develops a scalable methodology to quantify fare non-interactions and estimate associated revenue loss using automatically collected data from the Massachusetts Bay Transportation Authority (MBTA) bus network. By integrating Automated Fare Collection (AFC) and Automated Passenger Counter (APC) data, we calculate fare non-interaction rates at the stop-event level, identifying when passengers board without a recorded fare transaction. After data cleaning and quality control to remove systematic APC errors, the analysis reveals a systemwide non-interaction rate of 32
This study investigates the spatiotemporal relationship between land-use patterns and transit ridership along Bangkok’s MRT Blue Line using stepwise and geographically weighted regression (GWR) analysis. Drawing on ridership data and analyzing land-use composition within 500-meter buffers, the study identifies residential, commercial-office, and transport utility land uses as the primary predictors of both weekday and weekend ridership. Additionally, malls and markets emerge as significant predictors during late evening weekday and afternoon weekend periods, reflecting non-commuting travel behavior. The GWR results highlight substantial spatial heterogeneity, demonstrating how the influence of land use on ridership varies across time and location.
