Intelligent Public Transport System Design


19 Apr 2018

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An Intelligent Public Transport System for Smart City

  • Gurnoor Walia, Kuljit Kaur


Abstract -- Road safety has changed into a main subject for governments and automobile manufacturers in the last decade. The advancement vehicular technologies has privileged researchers, institutions and companies to target their efforts on improving road safety. new kinds of networks, such as for instance Vehicular Ad Hoc Networks (VANETs), have now been designed to assist communication between vehicles themselves and between vehicles and infrastructure. Smart cities embrace intelligent traffic management in which data from the Traffic Information Centre (TIC) infrastructures might be accessible at any point. In this paper we have listed the details of various features relating to intelligent transportation system.


Cities are complex, networked and continuously changing social ecosystems, shaped and transformed through the interaction of different interests and ambitions. Cities represent a promise for future years: a vision of creativity, opportunity, freedom and prosperity. More than half of the global population has become urban and surveys estimate this percentage may even grow towards 70% in 2050 [2]. The services are increasingly enabled by broadband infrastructures, Internet-based networked applications, wireless sensor networks, open data and open platforms. Within the last decade digital technologies have begun to cover our cities, working together to make the backbone of a big, intelligent infrastructure. Wireless telecommunications grids and broadband fiber-optic are supporting smart phones, mobile phones and tablets which can be increasingly affordable. Add to this foundation a uncompromisingly growing network of sensors and digital control technologies such as smart meters, all tied together by inexpensive, powerful computers and our cities are quickly fitting like computers in open air[3].

  1. Smart City

A smart city use digital technologies to boost the performance and wellbeing, to decrease costs and resource consumption, and also to engage more successfully and actively with its citizens. The core 'smart' sectors comprise energy, health care, transport, water and waste. It should be able it to respond more rapidly to needs of a city and global challenges than one with a simple 'transactional' association with its people.

Interest in smart cities is motivated by major challenges, including economic restructuring, climate change, ageing populations, the move to online retail and entertainment, and pressures on public finances.[4] The terms ‘intelligent city’ and ‘digital city’ are also used. [5][6].According to IEEE "A smart city brings together technology, government and society to enable the following characteristics: smart cities, a smart economy, smart mobility, a smart environment, smart people, smart living, and smart governance."[7].

1.3 Intelligent Transportation System

As India plans to take a leap forward with approval for smart cities, intelligent transportation is a must have technology. Intelligent transportation systems (ITS) are applications which, without embodying intelligence as such, intend to offer innovative services relating to traffic management and different modes of transport and enable users to be much better informed and make safer, more synchronized, and 'smarter' use of transport networks.

Intelligent transport systems differ in technologies used, from basic management systems such as traffic signal control systems; car navigation; container management systems; automatic number plate recognition; variable message signs or speed cameras to observe such applications, such as security CCTV systems; and to more complex applications that combine live data and feedback from numerous sources, such as weather information; parking guidance and information systems; bridge de-icing (US deicing) systems; etc.



The development of new vehicular technologies has shifted companies, researchers and institutions to focus their efforts on improving road safety. The evolution in wireless technologies has allowed researchers to style communication systems where vehicles directly take part in the network. Thus networks such as for instance VANETs are produced to facilitate communication between vehicles themselves and between vehicles and road side unit (infrastructure). Vehicular ad hoc network (VANET) is a technology which uses moving cars as nodes in a network to make a mobile network [10].

VANETs are becoming a useful consideration due to the various important applications related to traffic controlling road safety. Smart cities saturated in traffic want to minimize their transportation problems due to the increasing population that results in congested roads. VANET helps to fix this issue by improving vehicles' mobility and also helps at having more secured and sophisticated cities. VANETs provide easier communication facility among vehicles and also with fixed infrastructure. This can not merely improve the trail safety, but also gives benefits commercially.

Pollution reduction, accidents prevention, congestion reduction and safer roads are some of the benefits of VANETs. The development of an efficient system in VANETs has many important benefits, to the traffic police as well as to the drivers. Proper traffic alerts and updated information about traffic incidents will make safe driving, increase road safety and reduce the traffic jams in the city. It also helps to indentify where the traffic rules are violated. Furthermore, it also helps economically; real-time traffic alerting will reduce trip time and fuel consumption and therefore decrease pollution as well [11]. So it is definitely beneficial in many ways.


The smart city can utilize VANETs by having intelligent traffic lights (ITLs) set in the crossroads of a city. These ITLs gathering traffic information (e.g. traffic density) from the passing vehicles, updating traffic statistics (congestion) of the city and reporting those statistics to the vehicles to ensure that vehicle can select the very best path that is congestion free. Also, ITLs will send warning messages to vehicles in case accident occurs to prevent further collisions. As [14], the proposal manages traffic information to be able to avoid accidents, though the information here is gathered from the vehicles themselves so no more infrastructure is needed. Also the system could easily be utilized by the traffic information centre to style an adaptive traffic light system similar to [12] and [13]. The proposed system architecture [16] is as shown in figure 4.

Figure 4. The proposed System architecture [16] with intelligent traffic lights

It is assumed that vehicles have a global positioning system (GPS), aboard unit, full map information of the city including the exact position of the each ITL, to ensure that vehicles can very quickly select the nearest ITL. Warning message is of three types: yellow circle indicates that vehicle is independent and not communicating with every other vehicle, green circle indicates communication is made and messages transition is certainly going on red and signal indicates two vehicles come closer and there could be the chances of collision as shown in figure 4.

Inter-vehicular communication is presented based on an adaptive traffic signal control system [12]. This system reduces the waiting time of the vehicles at the square also results in decrease in waiting time at the signal. To realize this system, the concept of clustering is used to collect the data of the vehicles coming towards the intersection.

System that takes the control decisions based on the information coming from the vehicles is very well described by the authors [13]. Every vehicle is equipped with a short range communication device and controller nodes are placed in the intersection with traffic lights. This controller node at intersection acts as adaptive control signal system.

In [12] and [13] two adaptive traffic light systems based on wireless communication between vehicles and fixed controller nodes deployed at squares are designed. Both systems improve traffic fluency, reduce the waiting time of vehicles at squares and help to avoid collisions.

The work in [14] is a survey about multifunctional data driven intelligent transportation system, which collects a large amount of data from various resources: Vision-Driven ITS (input data collected from video sensors and used recognition including vehicle and pedestrian detection); Multisource-Driven ITS (e.g. inductive-loop detectors, laser radar and GPS); Learning-Driven ITS (effective prediction of the occurrence of accidents to enhance the safety of pedestrians by reducing the impact of vehicle collision);and Visualization-Driven ITS (to help decision makers quickly identify abnormal traffic patterns and accordingly take necessary measures). But, it requires large amount of memory to stores the videos.

The e-NOTIFY [15] system was designed for automatic accident detection, which sends the message to the Emergencies Center and assistance of road accidents using the capabilities offered by vehicular communication technologies. The e-NOTIFY system combines both V2V and V2I communications to efficiently notify an accident situation to the Control Center.

A technique of finding water-logging-prone areas is given in [8]. This recognition technique is principally based on the following steps. (i) Prediction of locations of low valleys in a sound prone 2D curve. (ii) Confidence score obtained from the calculation of valley area. The proposed solution could easily be integrated with participatory sensing for smart cities. If the smart-phone users voluntarily submit the GPS information received in their hand-held devices, the same can be used for water logging zone calculation. This can help the city authority to create a dynamic water logging prone map of the entire city.

In [9] researchers propose a radically different road pricing scheme to avoid and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in an area, they propose to employ a road pricing system over the entire city. Thus, the road pricing system can control the traffic flow in the whole traffic network of the city. Furthermore, the road costs are adjusted dynamically on the basis of the instantaneous traffic densities of every road in the city in order torapidly and efficiently control the traffic flow and to prevent the traffic congestion.

Geographical source routing is just a promising routing technique for VANETs, because adaptability for network dynamics and ability to take care of topology holes. In traditional geographical source routing algorithms a best-known neighbor, usually the neighbor nearest to another junction in a greedy fashion, is designated as the following hop. This method may cause two drawbacks: (1) the designated neighbor mightn't have the packet correctly and (2) non-neighbor nodes are never given opportunities to complete forwarding. In [1],two problems are solved by introducing the thought of opportunistic routing to geographical source routing. A new routing protocol, named Geographical Opportunistic Source Routing (GOSR), is developed. GOSR allows non-neighbor nodes as well as the best-known neighbor to become forwarder. The notification cost of opportunistic routing is minimized by enforcing a scope from which candidate forwarders are selected. Defer timers are adopted in order to avoid conflicts due to simultaneous transmissions by nodes in the designated scope. Simulation results also reveal that GOSR can substantially reduce hop count and also improve end-to-end delivery ratio remarkably.


It is significant to estimate the performance of any network in order to highlight any issues that may exist; the most appropriate way to accomplish this task is therefore to deploy simulations that provide the closest results to real-world annotations. Various simulation tools have been used to evaluate and simulate the performance of routing protocols in VANET.

5.1 Network simulator (NS2 and NS3 )

The NS-2 provides significant support for the simulation of TCP, routing and multicast protocols over wired and wireless networks.

The NS-2 simulator is written in C++ with an OTcl (Object Tool Command Language) interpreter as a command and configuration interface. C++ is fast to run but slower to change, making it appropriate for use in comprehensive protocol implementation.

NS3 is exclusively written in C++ and it is available for different platform such as Windows, Linux, Unix and OSX, with the coding limited to only a few hundred lines as opposed to 300,000 lines for NS-2. For the sake of huge network simulation,NS3 has come to support distributed and federated simulation tasks. NS-3 is free software available for researchers and developers in order to simulate internet protocols and huge systems in a controlled environment.

5.2 GlomoSim

GlomoSim was developed to simulate wireless network simulation. It was coded in Parsec, in which all new protocols need to be described. GlomoSim has the ability to run on SMP (shared-memory symmetric processor: memory simultaneously accessible by all programs) and to assist in dividing the network into separate modules, each running as a distinct process. This decreases the load on the CPU by dividing its workload. GlomoSim supports multiple wireless technologies. GlomoSim was developed to support million of nodes as a single simulation.

5.3 MOVE

The mobility model generator for vehicular networks is based on the Java programming language and is built on SUMO (Simulation of urban mobility). MOVE has greater consideration of traffic levels supported by GUI facilities.

Mobility trace files can be generated from the Google Earth or TIGER databases. Custom (random and user) graphs a real so supported, although the node movement is constrained to a grid in a random graph.

5.4 TraNs

TraNs (traffic and network simulator environment) is based on Java with a visualization tool to integrate SUMO and NS-2 and is specially designed for VANET (Traffic and network simulation environment) in a single module to support vehicular simulation. This can be accomplished by converting traffic files in to a dump file by SUMO. This file can then be read by NS-2.

5.5 VANET MobiSim

VANET MobiSim was developed to overcome the limitations of CanuMobiSim. It supports car-to-car and car-to- infrastructure communications, which support stop signs, traffic lights and activities based macro-mobility with the support of human mobility dynamics. TIGER, GDF and random and custom topology are used to obtain road and traffic topology. Vanet MobiSim uses a parser to obtain the topology from GDF or TIGER.

5.6 NCTUns

NCTUns (National Chiao Tung University Network Simulator) (WangandLin,2008) is built using C++ programming language with a high level of GUI support. The user has less need to be concerned about code complexity. NCTUns combines the traffic and network simulators in a single module, making a distinct vehicular network environment available.

NCTUns supports the ITS (intelligent transport system) environment by using automatic road assignment supported by the SHARPE-format map file. Vehicle movement can be controlled automatically.


In previous work researchers have designed a smart city framework for VANETs including intelligent traffic lights (ITLs) that transmit warning messages and traffic statistics. Simulation results reveal that the usage of ITLs in smart cities can not merely improve road safety but also the driver's quality of life. They have explained the way the ITLs gather traffic and weather conditions of the roads and how they update those statistics. The goal is that the driver's assistant device usually takes proper trip decisions, for instance in order to avoid congested roads, and therefore reducing the trip time and pollution as well. As a near future work, ITLs could communicate to passing vehicles indicating where would be the free parking spots in the city. With this specific information, the driver assistant device could indicate the driver where free spots are located. This technique could use a WSN to obtain the data about free parking spots and communicate it to the nearest ITLs. The ITLs could share that information although sub-network they form. This might save trip time, petrol and CO2 as a consequence, which helps to own sustainable smart cities. Also, statistics collected by the ITLs can improve data routing protocols selecting the road that offers an increased chance to forward a supply to the destination successfully. A VANET routing protocol that considers those statistics in its operation can also be designed.


[1] Zhongyi, L., Tong, Z., Wei, Y., and Xiaoming, L., “Poster Abstract: GOSR: Geographical Opportunistic Source Routing for VANETs,” Mobile Computing and Communications Review, Vol. 13, No. 1, January 2009

[2] United Nations, “World Urbanization Prospects, The 2007 Revision Highlights,” United Nations, New York, 2008.

[3] Schaffers, H., Ratti, C., and Komninos, N., “Special Issue on Smart Applications for Smart Cities – New Approaches to Innovation,” Journal of Theoretical and Applied Electronic Commerce Research, Universidad de Talca – Chile, Dec 2012

[4] Dept Business, "Challenges Faced by Cities and the Need for Smarter Approaches," pg-5, 2013

[5] Moir, "Challenges Faced by Cities and the Need for Smarter Approaches", pg-18, 2014

[6] Smart City,

[7] IEEE Smart Cities ,

[8] Choudhury, A.D., Agrawal, A., Sinha, P., Bhaumik, C., Ghose, A., and Bilal, S., “A Methodology for GPS-based Water logging Prediction and Smart Route Generation,” 12th International Conference on Intelligent Systems Design and Applications (ISDA), Kochi , 2012.

[9] Soylemezgiller, F., Kuscu, M., and Kilinc, D., “A Traffic Congestion Avoidance Algorithm with Dynamic Road Pricing for Smart Cities,” presented at IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, London, 2013

[10] Emmelmann, M., Bochow, B., and Kellum, C.C., “Vehicular networking: Automotive applications and beyond,” John Wiley and Sons, 2010.

[11] Ferrari, G., Busanelli, S., Lotti, N., and Kaplan, Y., “Cross- Network Information Dissemination in VANETs,” 11th International Conference on ITS Telecommunications, pp. 351-356, 2011.

[12] Maslekar, N., Boussedjra, M., Mouzna, J., and Labiod, H., “VANET based Adaptive Traffic Signal Control,” IEEE 73rd Vehicular Technology Conference (VTC Spring), pp. 1-5, 2011.

[13] Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V., and Iftode, L., “Adaptive Traffic Light Using Car-to-Car communications,” IEEE 65th Vehicular Technology Conference (VTC Spring), pp. 21-25, 2007.

[14] Junping, Z., Fei-Yue, W., Kunfeng, W., Wei-Hua, L., Xin, X., and Cheng, C., “Data-Driven Intelligent Transportation Systems: Survey,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, Issue 4, pp. 1624-1639, 2011.

[15] Fogue, M., Garrido, P., Martinez, F. J., Cano, J. C., Calafate, C. T., Manzoni, P., and Sanchez, M., “Prototyping an Automatic Notification Scheme for Traffic Accidents in Vehicular Networks,” Wireless Days (WD) IFIP, pp. 1-5, 2011.

[16] Khekare, G.S., Sakhare, A.K., “Intelligent Traffic System for VANET: A Survey,” International Journal of Advanced Computer Research (2277–7970) Volume-2 Number-4 Issue 6, December 2012.


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