A Survey on Urban Traffic Management System Using Wireless Sensor Networks
Abstract
:1. Introduction
2. Key Issues in Urban Traffic Management System
- Hierarchical road infrastructures for public transportation.
- Reliable information on real-time traffic should be provided to users and traffic management systems.
- The traffic control system should be fast in taking decisions.
- The highest priority to the emergency vehicles at intersections to save lives and property.
- The system has to detect road accidents.
- A smart city traffic system should provide security.
3. Overview
3.1. Sensing Evolution
3.2. Traffic Sensing Technologies
3.3. General Sensor Node
- A sensing module—This module acquires data.
- A processing and storage module—This module process the local data and stores it.
- A radio module—This module is for wireless data communication.
- A power module—This module is for energy supply.
3.4. Hierarchy of Urban Traffic Management Systems
Technology | Principles | Advantages | Disadvantages | Specific Equipment |
---|---|---|---|---|
Inductive loop | The inductive-loop sensor detects the vehicle or conductive metal object by sensing the loop inductance, which is dropped by inducing currents in the object. |
|
| Roadway sensors, lead-in cables, pull box and electronic unit in the control cabinet. |
RFID (Radio-frequency identification) | RFID technology uses radio waves to give-and-take data between a reader and an electronic tag attached to a vehicle for the purpose of tracking. |
| RFID only senses equipped vehicles at a point on the road. | Antenna (transmitter and receiver), Transponder, tag reader system, and computer. |
Microwave radar | The Microwave radar transmits signals in the recognition regions and captures the echoed signals from vehicles. The reflected signal is processed to find the speed and direction of the vehicle. |
| Continuous wave Doppler sensors are incapable of sensing immobile vehicles. | Antenna (transmitter and receiver), control unit and processor. |
Acoustic | Acoustic sensors detect audible sounds produced by vehicular traffic and there by vehicle presence, and speed are measured. |
| Vehicle count accuracy may be affected by cold temperature. | Transducer, filters, microphones, pre amplifier, storage equipment. |
Magnetometer | Magnetometers have sensors that sense the horizontal and vertical components of the Earth's magnetic field. |
|
| Magnetic probe detector, micro loop probes and control unit. |
Magnetic | A magnetic sensor detects the presence of a vehicle by measuring the perturbation in the Earth's magnetic field because of a ferrous metal object. |
|
| Magnetic probe detector, micro loop probes and control unit. |
Infrared | The infrared sensor illuminates the low powered infrared energy in the recognition regions and captures the echoed energy from the vehicles. The echoed energy is focused onto an infrared-sensitive material, which transforms the echoed and illuminated energy into electrical signals. These signals are processed and analyzed to obtain the presence of a vehicle. |
|
| Multi spectrum camera. |
Aerial/Satellite Imaging | This technology involves the use of either manned or unmanned helicopters in the sky to capture imageries of the ground and the imageries are transmitted to a workspace for investigation. |
|
| Helicopters, Analog color PAL camera and computer. |
Ultrasonic | An Ultrasonic sensor transmits ultrasonic waves and again collects the echoed waves from an object. It uses the time lapse between the transmitted and reflected sonic wave to identify the location of the object. |
|
| Transducers (Transmitter and Receiver), amplifier and oscillator. |
VIP (Video image processor) | This system normally consists of a camera, processor-based workstation for analyzing the images, and software for understanding the imageries and transforming them into traffic data. |
|
| Analog color PAL camera and image processing unit. |
Technology | Vehicle Count | Presence | Speed | Output Data | Classification | Multiple Lane, Multiple Detection Zone Data | Communication Bandwidth |
---|---|---|---|---|---|---|---|
Inductive loop | ✔ | ✔ | ✔ * | ✔ | ✔ & | Low to modest | |
Magnetometer | ✔ | ✔ | ✔ * | ✔ | Low | ||
Magnetic induction coil | ✔ | ✔ $ | ✔ * | ✔ | Low | ||
Microwave radar | ✔ | ✔ # | ✔ | ✔ # | ✔ # | ✔ # | Moderate |
Active infrared | ✔ | ✔ | ✔ @ | ✔ | ✔ | ✔ | Low to modest |
Passive infrared | ✔ | ✔ | ✔ @ | ✔ | Low to modest | ||
Ultrasonic | ✔ | ✔ | ✔ | Low | |||
Acoustic array | ✔ | ✔ | ✔ | ✔ | ✔ ^ | Low to modest | |
Video image processor | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | Low to high |
Technology | Description | Standard | Frequency | Range | Throughput | Feature |
---|---|---|---|---|---|---|
Wi-MAX | Standard for data transmission via radio waves. | IEEE 802.16 | 2–11 GHz | <10 km | <75 Mbps | High speed and serve number of users. |
ZigBee | Specification of a set of complex wireless communication protocols for use with low consumption digital radios, based on WPAN standard IEEE 802.15.4. | IEEE 802.15.4 | 2.4 GHz | <75 m | 250 Kbps | Mesh networks, Multiple protocol availability. |
Bluetooth | Standard for data and voice transmission between many devices via a safe and free radio link. | IEEE 802.15.1 | 2.4 GHz | Class 1: 100 m Class 2: 15–20 m Class 3: 1 m | v. 1.2:1 Mbps v. 2.0:3 Mbps UWB: 53–480 Mbps | Low power version available. |
UWB | UWB is merely a radio technology that can be used as part of an overall standard. | IEEE 802.15.3a | 3.1–10.6 GHz | 10 m2 m | 110 Mbps480 Mbps | Extremely fast transfer of files between servers and portable devices. |
Wi-Fi | System of wireless data broadcast over computational webs. | IEEE 802.11a; 802.11b/g/n | 5.8 GHz 2.4 GHz | <100 m | 11/54/300 Mbps | High speed and ubiquity. |
GSM | Typical system for communication via mobile phones including digital technology. | -- | 850/900/1800/1900 MHz | Dependent on service provider | 9.6 Kbps | Large coverage, High capacity and transmission quality. |
GPRS | Extended GSM for packet data communication. | -- | 850/900/1800/1900 MHz | Dependent on service provider | 56–144 Kbps | High resource utilization, Short access time. |
RFID | Uses radio waves to detect objects carrying tags. | -- | 125 KHz, 13.56 MHz, 902 to 928 MHz | Up to 3 m | 9.6–115 Kbps | Low cost. |
4. State-of-the-Art Review
4.1. Related Projects
Project Name | Objectives | Project Sponsor | Year of completion |
---|---|---|---|
Hong Kong ITS project [5]. | To perform an optimal traffic management. | Hong Kong Government. | 2010 |
A Distributed Instrument for Measuring Traffic in Short-Term Work Zones [6]. | To design, construct, and test a low-cost sensor network instrument to monitor traffic in work zones. | Research and Innovative Technology Administration, US | 2010 |
A Multi-Dimensional Model for Vehicle Impact on Traffic Safety, Congestion, and Environment [7]. | To use technology for creating a safe, efficient and greener environment. | Research and Innovative Technology Administration, US | 2011 |
Fast Lane: modelling and simulation of traffic flow [8]. | Prediction of the traffic flow. | Dutch traffic and transport laboratory for students, Dutch | 2013 |
Advanced Weather Responsive Traffic Management Strategies [9]. | To perform road weather management. | Research and Innovative Technology Administration, US | 2013 |
Adaptive Traffic Signal Control System (ACS Lite) for Wolf Road, Albany, New York [10]. | To dynamically adjust signal timing to meet current traffic demands. | New York State Department of Transportation | 2013 |
Advanced Traveler Information System (ATIS) for Indian Cities [11]. | To provide congestion information, alternate route, travel time and alert travelers about any accident. | Department of Electronic and Information Technology (DeitY), India | 2014 |
Agent- Based Traffic Management and Reinforcement Learning in Congested Intersections [12]. | To minimize travel time and reduce stoppage. | Research and Innovative Technology Administration, US | Start date: 2010-10-01 (In Progress) |
A Proof-of-Concept and demonstration of a High Definition, Digital Video Surveillance and Wireless Transmission System for traffic Monitoring and Analysis [13]. | To monitor and analyze the traffic through high definition video surveillance and broadcast system. | Research and Innovative Technology Administration, US | Start date: 2009-03-20 (In progress) |
4.2. Specific Architectures, Data Collection Schemes and Routing Algorithms
Reference | Proposed Approach | Outcome |
---|---|---|
Arbabi et al. [14] | Dynamic traffic monitoring system. | Collection of high quality travels time and speed. |
Mazloumi et al. [16] | GPS based tracking system. |
|
Bazzi et al. [17] Alexander et al. [18] | Vertical distance vector routing algorithms for timely data acquisition in VSNs. |
|
Bruno et al. [19] | Data collection (Greedy & PDC) Schemes for urban monitoring applications. |
|
Chao et al. [20] | RFID based intelligent traffic flow control system. |
|
Saqib et al. [21] | Symmetric double sided two way ranging algorithm. | Position and velocity of a moving vehicle are determined with less computation. |
Cabezas et al. [22] | WSN cross layer design approach to coordinate the transfer of packets. | Latency and jitter are improved. |
Choi et al. [23] | Delay-optimal VSN routing algorithm (OVDF). | Improved delivery performance of data packets in VSN. |
Friesen et al. [25] | Prototype of a cost effective Bluetooth traffic monitoring system. |
|
Liu et al. [26] | Vehicle-logo location algorithm. | Classification of vehicles. |
Zhou et al. [27] | User customizable data-centric routing. | Fast traffic information delivery. |
4.3. Congestion Avoidance Schemes
Reference | Proposed Approach | Outcome |
---|---|---|
Du et al. [29] | Circuit patrol and Greedy patrol algorithms to improve the estimation of traffic matrix. |
|
Knorr et al. [30] | VANET based strategies for improving traffic state estimation. |
|
Dragoi et al. [32] | Traffic model based on the use of cars to collect traffic data and several wireless traffic lights. |
|
Ahmad et al. [33] | A test bed for evaluation of traffic signal control algorithms. | Accurate measurement of execution times. |
Skordylis et al. [34] | Data spreading algorithms (D-Greedy, D-min cost) for optimizing the data delivery and the data acquisition. |
|
Abishek et al. [35] | Adaptive traffic flow algorithm. |
|
Eren et al. [36] | ZigBee based wireless system to assist traffic flow on urban roads. |
|
Laisheng et al. [37] | Traffic random early detection (TRED) algorithm for real-time scheduling of traffic. | Reduce Congestion. |
4.4. Priority-Based Traffic Management Schemes
Reference | Proposed Approach | Outcome |
---|---|---|
Rajeshwari et al. [38] Sireesha et al. [39] Shruthi et al. [40] Hussian et al. [41] Nabeel et.al. [42] | Implemented traffic control system. |
|
Chakraborty et al. [43] | Real-time optimized traffic management algorithm. | Effective management of high prioritized vehicles. |
Farheena et al. [44] | Traffic light control system and congestion avoidance systems are proposed. |
|
Zhou et al. [45] Tao et al. [46] | SIP/ZIGBEE based architecture for distributed traffic monitoring. | Remote communications and control operations of ITS distribution nodes are unified and simplified. |
Bottero et al. [47] | Magnetic sensor based traffic monitoring in logistic centers. |
|
Brahmi et al. [48] | Enhanced back-off section scheme for IEEE 802.15.4. |
|
4.5. Average Waiting Time Reduction Schemes
Reference | Proposed Approach | Outcome |
---|---|---|
Srivastava et al. [50] | Adaptive traffic flow algorithms Maximum intersection utilizations (MIU). Empty lane with green light (ELWGL). | The average waiting time: Orthodox policy: 26.7 cycles. MIU: 22.6 cycles. ELWGL: 6.5 cycles. |
Zhou et al. [51] | Adaptive traffic light control algorithm. |
|
Bhuvaneswari et al. [52] Zhou et al. [53] | Adaptive traffic signal flow control system. |
|
Bharadwaj et al. [55] | Vehicle count calculation and single toggle algorithm. |
|
Faye et al. [57] | Distributed algorithm to control traffic lights in urban areas. |
|
Al-Nasser et al. [58] Collotta et al. [59] | Smart traffic signal control algorithms. |
|
Collotta et al. [61] Wu et al. [62] | Dynamic traffic light control system based on WSNs and FUZZI logic controllers. |
|
Gomez et al. [63] | Traffic light state estimation using hidden Markov models. | Obtained 90.55% of accuracy in the detection of traffic light state. |
5. Challenges
6. Conclusions and Future Work
Author Contributions
Conflicts of Interest
References
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Nellore, K.; Hancke, G.P. A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors 2016, 16, 157. https://doi.org/10.3390/s16020157
Nellore K, Hancke GP. A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors. 2016; 16(2):157. https://doi.org/10.3390/s16020157
Chicago/Turabian StyleNellore, Kapileswar, and Gerhard P. Hancke. 2016. "A Survey on Urban Traffic Management System Using Wireless Sensor Networks" Sensors 16, no. 2: 157. https://doi.org/10.3390/s16020157
APA StyleNellore, K., & Hancke, G. P. (2016). A Survey on Urban Traffic Management System Using Wireless Sensor Networks. Sensors, 16(2), 157. https://doi.org/10.3390/s16020157