Abstract
This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper.
Highlights
The ever-increasing number of commuting people in cities has resulted in serious traffic congestion problems in urban roads
This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city
The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper
Summary
The ever-increasing number of commuting people in cities has resulted in serious traffic congestion problems in urban roads. Providing the potential commuter with a real-time schedule/plan of the bus trip and travel can, to a large extent, improve the reliability of public transportation and help with better travel planning. Beyond obtaining information about delays and expected trip duration, real-time route schedule and planner can help passengers choose optimum routes to their destination, taking into account, parameters such as number of stops, possible waiting times in bus stops and service frequency, which decide the duration and comfort of the trip. Real-time route-planners, though available for traffic conditions with lane discipline and homogeneity, are not pervasive for the public transit systems in countries like India. This is mainly due to the high level of uncertainty involved under such traffic conditions. To ensure that solutions are produced within a short period of time, the algorithm of choice here is the GA (Kumar et al 2010), which has proven its efficacy under similar conditions in other studies
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