A bus route evaluation model based on GIS and super-efficient data envelopment analysis

Abstract

When compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single factor and comprehensive evaluation models. Finally, the bus system in Shenzhen, China is used as a case study. The comparable significant results validate the capability of the proposed model. ARTICLE HISTORY

Short turning pattern for relieving metro congestion during peak hours: the substance coherence of Shanghai, China

“…aggregate and disaggregate demand) for obtaining optimal frequency and vehicle size. Temporal differences in passenger volumes can be addressed by providing a higher service frequency during peak hours, while the spatial demand unbalances justify the implementation of fleet assignment strategies, by increasing the service frequency on the high demanded route sections to adjust the service demand with the capacity supply [15,16]. To deal with this, Tirachini [17] developed a disaggregated short turning strategy with information at a station level.…”

A Novel Hierarchical Hybrid Model for Short-Term Bus Passenger Flow Forecasting

“…For current urban bus transport system, it faces more and more problems, such as improper arriving of buses, overcrowded or empty carriages, and so on, which cause passengers delay, bad ride experiences, and waste of transport resources. us, many enterprises try to adopt dynamically setting the timetable in real time based on the passenger flow variations and provide services in a proactive manner as opposed to a reactive manner with a predictive capability [1,2]. Short-term passenger flow prediction, the forecasting time interval not exceeding 60 minutes, is essentially important for setting the timetable in real time.…”

Reliability prediction model of further bus service based on random forest

“…Sun and Xu 16 have optimized for single bus line timetable based on hybrid vehicle size model. Sun and Xu 17 have evaluated the model based on geographic information system (GIS) and super-efficient data envelopment analysis. And a multistate-based travel time schedule model has been presented for fixed transit route.…”