Optimization of Public Transport Services to Minimize Passengers’ Waiting Times and Maximize Vehicles’ Occupancy Ratios

Abstract

Determining the best timetable for vehicles in a public transportation (PT) network is a complex problem, especially because it is just necessary to consider the requirements and satisfaction of passengers as the requirements of transportation companies. In this paper, a model of the PT timetabling problem which takes into consideration the passenger waiting time (PWT) at a station and the vehicle occupancy ratio (VOR) is proposed. The solution aims to minimize PWT and maximize VOR. Due to the large search space of the problem, we use a multiobjective particle swarm optimization (MOPSO) algorithm to arrive at the solution of the problem. The results of the proposed method are compared with similar results from the existing literature.

Advances in Public Transport Platform for the Development of Sustainability Cities

“…The research community is now turning its attention to different areas such as optimization and prediction [1][2][3][4][5]. As evidenced in references [2,3,5], which have analyzed travel time data to evaluate the performance of a public transport system.…”

“…The research community is now turning its attention to different areas such as optimization and prediction [1][2][3][4][5]. As evidenced in references [2,3,5], which have analyzed travel time data to evaluate the performance of a public transport system. Others have focused on the demand for different modes of transportation and interaction among them, including a proposal for minimizing the passengers' waiting times and maximizing the vehicles' occupancy ratios.…”

Exploratory Data Analysis and Data Envelopment Analysis of Urban Rail Transit

“…Big data can gather, store, and process large amounts of heterogeneous, large-scale data to assist regulators, cities, transport operators, and travelers to improve the efficiency, regulation enforcement, and sustainability of their mobility solutions. So far route planning (e.g., Masivo model [48]) and public transport timetable optimization [49] are based on simulation models which can greatly benefit from the incorporation of big-data analysis into their models. Additional big-data applications are personalized route planning and smart taxation (based in the polluters-pay principle) such as dynamic tolling depending on the specific CO 2 footprint of cars and their usage (kilometers) in city centers, where air quality has one of the highest impacts on people's health.…”

Joint optimization of headway and number of stops for bilateral bus rapid transit

“…When optimizing the operation of transit, although some studies optimized only headway, more studies focus on the joint optimization of headway with some other factors, including load factor, vehicle type, stop, fare, etc. [25][26][27][28][29]. Typically, many studies consider elastic demand when designing fares because passengers will be sensitive to fares [30][31][32][33].…”