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TRANSPORT ISSN 1648-4142 / eISSN 1648-3480 2020 Volume 35 Issue 2: 156–167 https://doi.org/10.3846/transport.2020.12477 DYNAMIC TRIP PLANNER FOR PUBLIC TRANSPORT USING GENETIC ALGORITHM Abhishek BASU, Bharathi RAJA, Rony GRACIOUS, Lelitha VANAJAKSHI* Dept of Civil Engineering, Indian Institute of Technology Madras, India Received 15 December 2016; revised 3 April 2017, 12 October 2017; accepted 26 November 2017 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 subse- quently 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 seg- mented 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. Keywords: dynamic trip planner, genetic algorithm, global positioning system, public transportation, route-planner, static network, real-time data. Notations CSA – connection scan algorithm; der-utilized because of poor service quality; public buses ETA – expected time of arrival; that connect various parts of the city efficiently, for exam- GA – genetic algorithm; ple, are often not time-bound and are unreliable. Frequent GPS – global positioning systems; delays in bus travel are caused by various reasons, the pre- LB – lower bound; dominant among them being traffic congestions, and such delays make travel planning very tedious. While the delay MRTS – mass rapid transit system; itself may not be avoidable, what makes for nuisance value MTC – metropolitan transport corporation; is that the travel status is not communicated to the com- SF – solution file; muter, who has to wait indefinitely for the service. STT – service and time table; Providing the potential commuter with a real-time TSF – temporary solution file; schedule/plan of the bus trip and travel can, to a large UB – upper bound. extent, improve the reliability of public transportation and help with better travel planning. Beyond obtaining infor- Introduction mation about delays and expected trip duration, real-time route schedule and planner can help passengers choose The ever-increasing number of commuting people in cities optimum routes to their destination, taking into account, has resulted in serious traffic congestion problems in -ur parameters such as number of stops, possible waiting ban roads. A common-sense approach to avoiding traffic times in bus stops and service frequency, which decide snarls is to encourage people to use public transportation the duration and comfort of the trip. services. Although there are extensive public transporta- A route-planner is typically designed using travel data tion services in almost all cities in India, they remain un- obtained from the road. This data could be historic (static) *Corresponding author. E-mail: [email protected] Copyright © 2020 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unre- stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Transport, 2020, 35(2): 156–167 157 or real-time. A static route-planner can assist the passen- be the same across all passengers. Moreover, passen- ger in planning the trip in advance. A real-time route- gers are trying to minimize time. The repercussions planner could help the passenger optimize a trip based of this assumption are minimized by the design of on real-time updates; for example, unexpected changes in the result display – by ensuring sufficient alternatives bus schedules and operation can be communicated in re- are presented to the user and it is the user’s choice al-time, which can enable the commuter to make on-the- to select the alternative that satisfies the user’s goals; spot alterations to the trip details. The best route-planner – the exact itinerary is not fixed “apriori” and it is as- is one that combines both static and real-time operation. sumed that the user is willing to change their itiner- Most earlier studies on route optimization of multi- ary if the solution presented during the initial query modal travel have treated route optimization as non-de- ceases to be optimal. The user’s attractive set is gov- terministic polynomial-time hard problems that are time erned by the solutions presented by the route-planner consuming to solve using exact algorithms. To enable and the user’s pre-defined preferences such as maxi- faster solution, Deng and Hu (2011) used GA for route mum number of transfers; optimization. The stochastic search techniques of GAs – in the static implementation of the route-planner, it use natural selection and evolution to solve optimization is assumed that the buses arrive as per a schedule problems (Hiu 1996; Zhao, Zeng 2006). based on past data. For the real-time implementation Real-time route-planners, though available for traffic of the route-planner, the assumption is that buses ar- conditions with lane discipline and homogeneity, are not rive randomly, and the estimated arrivals are based pervasive for the public transit systems in countries like on predicted values at the time of query. In case the India. This is mainly due to the high level of uncertainty query is made before the particular run of the bus, involved under such traffic conditions. The variability in then values used in the static implementation are such traffic conditions with heterogeneity and lack of lane used instead. discipline is much higher than the lane-following, ho- In GA, the search space comprises the space of all fea- mogeneous traffic. The route-planner developed in this sible solutions, each point in which represents a unique study, takes into account the aforementioned inaccuracies solution (Obitko 1998). The fitness of this point is defined and variability by applying suitable bounds to the input according to the objectives of the problem. A string of received from the prediction algorithm to ensure the solu- elements (values) in a particular order, called the chromo- tions presented to the user are stable and do not change some, represents each solution of the GA. The Chromo- rapidly with time. Moreover, to ensure that solutions are some representation used in this study is shown in Figure 1, produced within a short period of time, the algorithm of where nodes 2, 12, 14 and 20 represent bus stops, and choice here is the GA (Kumar et al. 2010), which has prov- 51000, 54000, 56000 represent unique bus routes within en its efficacy under similar conditions in other studies. the network. The elements themselves are called “genes” and the specific representation of the chromosomes is 1. Methodology the “encoding”. A “generation” of population is the set of This real-time dynamic route-planner presented in this chromosomes generated during a single iteration of GA. study provides alternatives that could help a passenger to The application of genetic operators such as “selection”, seek the itinerary that requires the shortest time to travel “crossover” and “mutation” on successive generations of from one node (origin) to the other (destination) within population, enhances the fitness of individual solutions. the network, under real-time conditions. The planner GA has been used sporadically in bus network optimi- seeks to provide the solutions in the shortest possible time. zation problems. Borole et al. (2013), for example, devel- Secondary objectives include a framework for scalability oped route-planners using K-shortest path algorithm with of the algorithm and ensuring that the solutions are rea- GPS data, to obtain multi-criterion shortest path. Nanay- sonably stable to absorb the errors inherent in the predic- akkara et al. (2007) developed GA-based route-planners tion algorithm whose output the route-planner utilizes to for large urban street networks in Singapore. In their provide the solutions. The assumptions of the model are: work, chromosome encoding with each gene represented a – the bus capacity is considered to be infinite. This as- node ID. Abbaspour and Samadzadegan (2011) described sumption is acceptable since the acceptable crowding the use of GA-based multimodal path computation in level varies greatly among different passengers; while time-dependent personal tour planning and scheduling. some passengers limit their choice to sparsely occu- This study developed a non-binary chromosome encod- pied buses, others might be willing to travel when ing scheme with alternate genes representing node and the bus is occupied close to its capacity. This assump- mode. Chen et al. (1999) also reported the development of tion is also convenient since currently bus crowding multimodal route-planner using offline data. Jariyasunant data is not available for most bus routes in the system considered; 2 51000 12 54000 14 56000 20 – the current study assumes all passengers to be homo- geneous and therefore value of time is considered to Figure 1. Chromosome representation 158 A. Basu et al. Dynamic trip planner for public transport using genetic algorithm et al.

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