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2016 International Conference on Emerging Technological Trends [ICETT]

Real-Time Smart Traffic Management System for Smart Cities by Using Internet of Things and Big Data

Patan Rizwan, K Suresh, Dr. M. Rajasekhara Babu

School of Computer Science and Engineering, VIT University,

Vellore, India-632014. [email protected], [email protected], [email protected].

Abstract - Smart Traffic management system (STMS) is a one of the important feature for smart city. Currently traffic management and alert systems are not fulling needs of STMS. It is more expensive and highly configurable to provide better service for traffic management. This paper proposes a low cost Real-Time smart traffic Management System to provide better service by deploying traffic indicators to update the traffic details instantly. Low cost vehicle detecting sensors are embed in the middle of road for every 500 meters or 1000 meters. Internet of Things (IoT) are being used to acquire traffic data quickly and send it for processing. The Real time streaming data is sent for Big Data analytics. There are several analytical scriptures to analyze the traffic density and provide solution through predictive analytics. A mobile application is developed as user interface to explore the density of traffic at various places and provides an alternative way for managing the traffic.

Index Terms- Internet of things, Big Data, Smart Cities, Smart Traffic Management System.

I. INTRODUCTION

A smart city is an urban improvement vision to incorporate various Internet of Things (IoT) and information and communication technology (ICT) arrangements in a safe style to deal with a city's advantages – the city's benefits incorporate, yet are not constrained to, neighborhood offices data frameworks, schools, libraries, transportation frameworks, clinics, power plants, water supply systems, waste administration, law requirement, and other group administrations. The objective of building a smart city is to enhance personal satisfaction by utilizing innovation to enhance the proficiency of administrations and address occupants' issues. ICT permits city authorities to communicate straightforwardly with the group and the city base and to screen what is going on in the city, how the city is developing, and how to empower a superior personal satisfaction. Using sensors coordinated with ongoing checking frameworks, information is gathered from nationals and gadgets

then handled and broke down. The data and information assembled are keys to handling inefficiency.

Background

Activity congestion on street systems is only slower speeds, expanded excursion time and expanded lining of the vehicles. At the point when the number of vehicles surpasses the limit of the street, activity congestion happens. In the metropolitan urban communities of India activity congestion is a noteworthy issue. Movement congestion is brought about when the interest surpasses the accessible street limit. This is known as immersion [1]. Singular episodes, for example, mischances or sudden braking of an auto in a smooth stream of overwhelming activity have undulating impacts and cause car influxes [2]. There are even serious security issues in movement framework because of hostile to social components which likewise prompts stagnation of activity at one spot. In nation like India, there is a yearly loss of Rs. 60,000 crores because of congestion (counting fuel wastage). Congestion in India has additionally prompted moderate velocities of cargo vehicles, and expanded holding up time at checkpoints and toll squares [3]. The normal pace of vehicles on key halls like Mumbai-Chennai, Delhi-Chennai is under 20 kmph, while it is negligible 21.35kmph on Delhi- Mumbai stretch. According to the vehicle organization of India and IIM, India's cargo volume is expanding every year at a rate of 9.08% and that of vehicles at 10.76%, yet that of street is just by 4.01%. This has brought about lessened street space as per the quantity of aggregate vehicles [3]. The normal fuel mileage in India is just 3.96kmpl. The significant explanation behind this is movement blockage [3]. India is the second most populated nation after China in Asia, hence with expansion in populace, the quantity of vehicles additionally increments [4]. The monetary development has surely has affected urban activity. As the pay rises, increasingly individuals start to go for cars as opposed to bikes [5]. Hence there is a need to oversee movement adroitly as the administration of activity with the traditional

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2016 International Conference on Emerging Technological Trends [ICETT]

route, for example, the flagging framework is not having a noteworthy impact in checking congestion [19] of vehicular movement. In figure 1 shows that placing sensors for vehicle detection

Fig. 1: Smart central wise Traffic management system [12]

Motivation

Smart Cities are day to day increasing and inside cities transportation play’s key role are also increasing heavily. For better traffic control system [16] needed to control the vehicles traffic it means reducing time delay, now currently traditional, semi-automation only are using to controlling vehicles traffic. Traffic blockage has turned into a noteworthy issue in each extensive city of the world. To guarantee a dependable transportation framework it is imperative to have a wise traffic control framework. The initial step to do that is to get traffic information. Traffic information may originate from various sensors. A few cases are utilization of affectation circle, infra- red light sensor, optical stream flow etc. are not used for current traffic control system now our approach applying technologies called Internet of Things and Big data.

Contribution

A summarized workflow of the paper as follows:

a. Study current various types of traffic management system techniques.

b. Review on what are the disadvantages are facing by using Existing Traffic Management system.

c. Theoretical technical deployment process shown how we can deploying sensors and interaction provided by system.

d. Practical deployment and experimental evaluation procedure for system to measure traffic conditions and provide frequent update for users.

e. Finally, our approach is provided great impact while comparing to old approaches. It gives user friendly traffic oriented updates interaction to travelers.

Paper organization

In this paper discussing by the Smart city traffic management system. Section 1 contain related work, section 2 contain Problem statement, section 3 contain Theoretical Deployment Process, section 4 contain practical evaluation model, section 5 contain Experimental Result and their discussion, section 6 contain Conclusion and future work and finally references.

II. RELATED WORK

In existing status of the smart city transport management system. In [12] this paper proposed a strategy for deciding traffic blockage on streets utilizing picture preparing procedures and a model for controlling traffic signals in light of data got from pictures of streets taken by camcorder. We separate traffic thickness which compares to aggregate range possessed by vehicles out and about as far as aggregate sum of pixels in a video outline as opposed to figuring number of vehicles. We set two parameters as yield, variable traffic cycle and weighted time for every street in view of traffic thickness and control traffic lights in a consecutive way it is very time complex as well as expansive.

Consequently, it is high time to adequately deal with the traffic jam [15] issue. There are different strategies accessible for traffic administration, for example, video information analysis, infrared sensors, inductive circle recognition, remote sensor system, etc. Every one of these techniques are successful strategies for keen traffic administration. Yet, the issue with these frameworks is that the establishment time, the expense caused for the establishment and support of the framework is high. Henceforth another innovation called Radio Frequency Identification (RFID) is presented which can be combined with the current flagging framework that can go about as a key to brilliant traffic administration [13] continuously. This new innovation which will require less time for establishment with lesser expenses when contrasted with different techniques for traffic blockage administration. Utilization of this new innovation will prompt decreased traffic blockage. Bottlenecks will be distinguished early and consequently early preventive

2016 International Conference on Emerging Technological Trends [ICETT]

measures can be taken in this manner sparing time and cash of the driver.

In [6] author introduced model called dynamic traffic monitoring system. It using their various parameter factors towards collecting Gathering of high quality travels time and speed. It means that various affecting factors are should counting. In [7] author invented that GPS based vehicle tracking system. It provides to decreasing the short distance traveling’s. as well as same content in [8] focused on various factors are to be considering timely data acquiescing by using VSNs vector distance routing algorithm. It provides the high reliable communication [18]. It covering the range maximum distance fir collecting data. In [9,10] greedy and PDC based data collection schemes for application monitoring in urban areas. By using these logic, we have to reducing redundancy information as well as it consuming very less band width. In [11] this author created an RFID based intelligence traffic control system. It controlling traffic flow, reducing accidents in traffic places and remote location transmission.

III. PROBLEM STATEMENT

A Novel Technical support for traffic control system for better emerging traffic management system for smart cities. Deploying booming technologies like Internet of Things and Big data. User friendly App based traffic updates, status of road based vehicle strength etc. interaction provided by using these technologies.

IV. THEORETICAL DEPLOYMENT PROCESS

The portability model utilized for regenerations depends on the comfortable driving model (CDM) in [17]. It is a traffic cell robot where space is discretized in units of 1.5 m and time in interims of 1 s. The position and the speed of a vehicle named as n are given by and , individually. Vehicles are named downstream such that the vehicle before n is named n + 1. For accommodation, extra variables and are

acquainted with signify the spatial and worldly progress between vehicles n and n + 1. The model incorporates expectant impacts by considering the status of the first vehicle's brake light

by suspecting its speed

(1)

and

by computing the compelling separation as using eq. (1)

(2)

Calculating gap between the vehicles . Accessing priority calculation by each vehicle and their speed calculation [20] by using the below conditions.

In here mention that first calculating the acceleration of the vehicle from sensor it is arrive rate of speed to be calculated by using below condition

Vehicle motion calculation using the below formula

(3)

Fig. 2. Theoretical flow strategy for Real-Time traffic management system.

2016 International Conference on Emerging Technological Trends [ICETT]

In Figure 2 shows flow of the process Real-Time traffic management system. plan a straightforward activity calculation [14] to execute in the movement framework. The activity focuses are dealt with as autonomous areas. We take the movement densities of various streets at a specific time as info. In light of out information, we have created two yields. Movement Cycle ( ) is the aggregate time required for one complete revolution of the sign lights at any activity point. The activity cycle is taken as an element of aggregate movement thickness ( ) of vehicles given as (4).

= f ( ) (4)

The denser the activity, longer is the movement cycle. This strategy is connected for more cycle span when there is more movement (as in surge hour) so that more vehicles can breathe easy. At the point when there is less movement, the activity cycle is abbreviated with the goal that vehicles don't need to sit tight for a drawn out stretch of time in sign moves. The second parameter is weighted time portion

V. PRACTICAL EVALUATION MODEL

In practical approach it consists the three different modules are adding.

Experimental Setup

For experimental setup three different modules are there for overall application designing. Based on below figure (3) shows deploying technologies on root phase.

Fig. 3. Technical overflow for interconnection to the technologies.

Internet of things module

Approach is to completely IoT based vehicle information gathering system. Intel IoT kit with all latest features and vehicle detection sensors. Connected the sensors based on our criteria we deploy on road ½ km or 1 km and more it depends best is to deploy very near distance for getting better results. At least 10 sensors are connected in each other and it communicates to the single IoT kit. All kits are connected to the network access sharing information among the Internet. It continues monitoring for vehicles and updates are sending to the big data storage and analytics.

Big Data Analytics Module

It Receives the sensors information with sensor Id. Compute all the information performing analytics operations. Various factors are considering for calculating individual sensor strength and add each other sensor entry and leaving vehicle details road capacity. Etc. factors are considered compute analyzed report are to be produced it make ready through access by using internet either mobile APP or internet browser. Here apply various forms are approaches [21] are connected with latest real-time streaming data processing mechanisms are used.

User Interaction Modules

In this module consists of the latest analytics and decision tools are providing for travelers. Capacity of road number of vehicles are there status everything shown accessing internet. Multiple way’s user wants to access the information example mobile APP, internet browser throw enabling GPS on Device, etc. In user point of view very faster interaction and fast data processing are to be done by using background as big data stream analytics. For better and faster real-time data stream computing as well as analytics on top of that we apply

VI. RESULTS AND DISCUSSION:

In considering a multiple possibility are taking for the shown the potentiality of the application. First vehicle motion detection sensors deployed road. Connected with Intel IoT board figure 4 showing the vehicle density and arriving rate of the vehicles. We consider one road how the distance wise vehicles are accessing.

For correspondence separations of 80 m or more, we set m = 0.75. For littler separations, in any case, we set m = 1.5 with respect to short separations an unmistakable observable pathway is liable to exist. We have picked a dynamic roadway situation with exact limit conditions. Vehicles proceed onward a 12-km-

2016 International Conference on Emerging Technological Trends [ICETT]

long two-lane thruway fragment with an entrance ramp and exit ramp. This fragment relates to a segment of the German Autobahn A044 between the urban communities Figure 5 demonstrates the geometry of the considered thruway area. To model open limit conditions, including the inflow and outpouring by means of the two slopes, we utilized the indicator information of November 4, 2010, which demonstrate an unconstrained breakdown in morning crest hour movement (see Figure 6).

Fig. 4. vehicle access rate per each rode in periodical change rate.

Fig. 5. IoT Device enable rate for vehicle density and arriving rate.

The time arrangement of indicator D06 as portrayed in Figure 6 is excellent for all indicators upstream the entrance ramp: After the breakdown happened at around 7:15 A.M., upstream vehicles drawing closer the entrance ramp need to back off bringing on a congested movement design that ventures upstream. Stream in every bearing is around 36 000 vehicles for every day with extra 7000 vehicles joining the street by means of the on–off-incline framework. Vehicles leaving the framework as required by the limit conditions are chosen haphazardly with no inclination for (non-) conveying vehicles. Along these lines, the exit ramp has no impact on the proposed methodology, yet it is important to incompletely make up for vehicles entering by means of the entrance ramp. Essentially, entering vehicles are conveying as indicated by the given infiltration rate. The entrance ramp builds the likelihood than 35% from 819 to 527 s. Also, the lower bend in Figure 5 demonstrates the standard deviation of the watched travel times. The standard deviation of travel times can be comprehended as a measure of travel time dependability and shows comparable reliance on the infiltration rate; with one in four vehicles being capable to convey, the standard deviation's worth measures as it were 96 s, contrasted and more than 200 s when correspondence is killed.

Fig. 6. Average travel time for a different penetration rate with multiple travel time ranges.

To decide the normal increment of time deferral in each situation, we ascertained an ideal travel time. To do as such, we begun a vehicle at every second of the reenacted day on an unfilled street and recorded the relating travel time. Taking into record the offer of trucks and trucks' expanded travel time

2016 International Conference on Emerging Technological Trends [ICETT]

because of their lessened most extreme speed, one acquires an ideal normal travel time of 425 s. This time serves as a kind of perspective quality in Figure 6, which demonstrates how much the situations go amiss from the perfect condition with no associations.

VII. CONCLUSION AND FUTURE WORK

Our paper proposed a low cost Real-Time smart traffic Management System to provide better service by deploying traffic indicators to update the traffic details instantly. Low cost vehicle detecting sensors are embed in the middle of road for every 500 meters or 1000 meters. IoT are being used to acquire traffic data quickly and send it for processing. The Real time streaming data is sent for Big Data analytics. There are several analytical scriptures to analyze the traffic density and provide solution through predictive analytics. A mobile application is developed as user interface to explore the density of traffic at various places and provides an alternative way for managing the traffic. Moreover, our approach is provided a better result while comparing to the existing systems.

In future work now current system only detecting vehicle but not vehicle types. Adding more advanced sensors using for detecting nature of vehicle capacity of vehicle. Basic way of analytics big data analytics performed. Applying various advanced approaches to create more flexible to travelers.

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