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

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.

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

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

The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

The emphasis on transportation for urban, suburban and interstate systems has brought about a need for reconsidering the types of models which are used for such systems. Two primary problems appear to be common in most large‐scale applications involving the amount of data and the time required to use the model as part of a real‐time system. The systems considered are traffic systems which can be classically modeled using vertices and edges to form a static network over which a vehicle or vehicles are to be routed. The problem that is solved is one of developing a model to be used for the storage of the connection and/or cost information of the system.

The UK road network faces critical problems over the next decade. Severe road congestion made worse by ever increasing vehicle numbers and consequent requirements for road maintenance and repair form a vicious circle, while provision of new roads remains an increasingly expensive and environmentally unacceptable solution. But as billions of pounds are wasted annually in traffic jams, air and sound pollution continue to affect our health and pedestrian safety and emergency service access suffer, we must all recognize that to prevent future chaos actions must be taken now. Some of these measures may be unpalatable to a nation dedicated to the motor car. The use of dedicated electronic systems including route guidance, incident avoidance, in‐vehicle signing, head up displays and anticollision radar have the potential to revolutionize every facet of road transportation, and are already starting to provide drivers with new standards of safety, comfort, security, reliability and efficiency. However to combat fully the growing crisis threatening our road system radical solutions must be sought to offer answers for the better management of the UK's road infrastructure for the next decade and beyond.