Integrated mode of transport: a predictive model for route guidance

Abstract

Purpose

The purpose of this study is to provide real-time route guidance within city to help commuters.

Design/methodology/approach

In urban areas to avoid road congestion and to reach the destination on time, intelligent transport system (ITS) utilizes recent advanced technology. To support this, existing route guidance system (RGS) suggests alternative route to commuters. However, ITS requires a system which suggests the alternative route along with the mode of transport such as public, private, taxi services etc. Integrated mode of transport (IMT) implemented in this paper guides the commuters of urban area with the best mode of transport. Inputs to our IMT predictive model are the commuter's choice of (1) minimum travel time (2) minimum cost (3) flexible route and (4) less traffic intensity along with source and destination locations. Based on these user inputs, IMT predictive model suggests optimal mode of transport. In this paper to implement the IMT model, we have considered the transport facility available in Bangalore, a city in India. The city has metro train, bus and taxi services available to the commuters. Implementation is divided into two parts. In the first part, the model checks for the end-to-end connectivity/availability of metro train facility. If metro train connectivity exists, the model concludes this as the best mode of travel. In the second part, for the routes which are not connected by metro train, the optimal mode of transport through road network will be suggested. In the first part, to check the existence of metro train along the routes between source and destination, location-IQ API is used. In the later part, to suggest transport along the road network, Q-learning algorithm of reinforcement learning technique is used.

Findings

The findings are the predictive model algorithm to find the best mode of transfer and reinforcement model used in real time route guidance system.

Originality/value

This is a new Idea, not proposed in any research work.

Citation

Ravish, R. and Rangaswamy, S. (2022), "Integrated mode of transport: a predictive model for route guidance", International Journal of Intelligent Unmanned Systems, Vol. 10 No. 4, pp. 289-301. https://doi.org/10.1108/IJIUS-09-2020-0040