Public transit planning and scheduling based on AVL data in China

Abstract

The public transit operations planning process commonly includes the following activities: network route design, service planning (frequency setting and timetabling), and scheduling (vehicle scheduling, crew scheduling, and rostering). However, the network route design is generally the only one widely recognized, while service planning and scheduling are often ignored in China. This leads to the lack of elaborate timetables and schedules, hence, transit operation is often in disorder with high operating costs. To raise the service level and the utilization of resources, this paper presents an applied study for three cities in China, focusing on the enhancement of the recognition and execution of public transit planning and scheduling. A comprehensive framework of public transit planning is first proposed, which is composed of three traditional Chinese items (i.e. network route design, land use for depots, and deployment of vehicles) and the following newly added items: intelligent public transit system (IPTS) planning, service planning, and scheduling. This is pioneering work in China, in which an IPTS plan is conceived and a new vehicle scheduling approach based on AVL data is developed. Experiments during actual projects show that vehicle schedules with high on-time probability and low cost were compiled, while the essential input parameters such as headways and trip times were set automatically. It is anticipated that the fruits of this research and the practical experience obtained would be of great benefit in improving service and management levels and resource use in public transport in China and some other developing countries.

Combining ITS and optimization in public transportation planning: state of the art and future research paths

“…Still, few studies on operational decisions have exploited AVL data, while so far, the only problem addressed has been the generation of optimal vehicle schedules. The associated Vehicle Scheduling Problem (VSP) is that of the optimal allocation of vehicles to trips, based on precompiled timetables, yet in the presence of AVL data, operators can devise more robust vehicle schedules based on observed trip times [71][72][73][74]. Indeed, AVL data have allowed for extracting periods of homogeneous running time [72] and trip time probability distributions [73,74] to determine reliable vehicle schedules that enhance service reliability;…”

“…Inevitably, benefits in modeling accuracy obtained by exploiting ITS data naturally depend on the quality of the data utilized [46,105]. The latter is dictated by the technical specifications of the ITS system deployed [72] as well as the archiving process [8]. Indeed, benefits stemming from ITS-supported decision making are intertwined with the data reporting standards adopted by operators.…”

“…Indeed, benefits stemming from ITS-supported decision making are intertwined with the data reporting standards adopted by operators. For instance, older/less advanced AVL systems produce reports which contain vehicle trajectory data, lacking stop-level information [72], calling for matching algorithms to couple raw location data to route maps and schedules [8,44]. Regardless of the type of ITS, a series of similar data manipulation procedures have been proposed to remove problematic entries and impute missing values [2,4,44,96].…”

Vehicle scheduling based on variable trip times with expected on‐time performance

“…As well known, AVL systems have been widely equipped and a large number of AVL data have been accumulated. Applying statistical tools, the trip-time distribution or CDF for each period (peak or nonpeak) throughout a day can be obtained as addressed in Xu and Shen (2012) and Shen et al (2015). Based on the CDF, the minimum and maximum values of the trip time can be obtained, which correspond to the CDF values 0 and 1.…”

“…The VSP has attracted much research interest since the 1960s, and a series of solution methods have been developed (Freling et al., ; Pepin et al., ; Hassold and Ceder, ; Shen et al., ; Zuo et al., ). Early research focused on the VSP with a single depot (SDVSP) (Gavish and Shlifer, ; Bodin et al., ).…”