|Sony Sumaryo||School of Electrical Engineering, Telkom University, Jl. Telekomunikasi Terusan Buah Batu, Bandung 40257, Indonesia|
|Kalamullah Ramli||Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Abdul Halim||Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Endra Joelianto||Instrumentation and Control Research Group, Faculty of Industrial Technology, Institute Technology of Bandung, Ganesha 10, Bandung 40132, Indonesia|
Intelligent Transportation System (ITS) is the synergy of information technology, real-time control, and communication networks. The system is expected to perform more complex traffic arrangements, in particular, traffic management of Emergency Vehicles (EV) such as fire trucks, ambulances, and so forth. Implementation of traffic management using only Traffic Signal Pre-emption does not give enough space for an EV to cross an intersection safely, especially on streets where there is only one lane. This paper proposes a model of accelerated emptying of traffic in front of EVs. Accelerated emptying model uses historical approach, based on current characteristics of traffic. For example, if the normal vehicle speed is equal to the EV speed before accelerated emptying, the system indicator will be 0%, thereby indicating no need for accelerated emptying. Similarly, a negative system indicator result means an accelerated emptying process is not necessary. However, if the system indicator is close to 100%, this result indicates accelerated emptying is necessary.
Acceleration discharge; Emergency vehicle; Historical data; Model
Researchers around the world have concerned themselves with how to efficiently exploit capacity of the existing transportation infrastructure (Sumaryo et al., 2014). Intelligent Transportation System (ITS) was proposed to solve traffic problems; it consists of information technology, real-time control, and network communications. ITS researchers have received considerable attention worldwide, including: Kim and Mahmassani (2015); Lim (2016); Lopez-Garcia et al. (2016); Loorbach and Shiroyama (2016); Mannion et al. (2016); Hassn et al. (2016).
The transportation system is a complex and dynamic system that is difficult to model with precision (Li et al., 2007). However, without proper modeling, characteristics of the transportation system cannot be properly identified for the purpose of evaluating existing methods and identifying potential problems.
Traffic flow modeling, using queuing theory, has been performed by numerous experts (Yang & Yang, 2014; Fuyu et al., 2014; Sumaryo et al., 2015).One traffic problem identified in traffic flow modeling is the management of Emergency EV such as fire trucks and ambulances (Sumaryo et al., 2014). Signal control for handling EV requires a pre-emptive control, designed and operated to give the most important classes of vehicles the right-of-way through a signal or intersection (Kittelson & Associates, Inc., 2008). Many researchers have examined the appropriate method for pre-emptive control (Huang et al., 2011; He et al., 2011; Shruthi & Vinodha, 2012; Goel et al., 2012; Kuang & Xu, 2012; Chakraborty et al., 2014). Research shows that some benefits associated with traffic signal pre-emption include: improved response time/travel times for EV; improved safety; and, reliability for vehicles receiving pre-emptive right-of-way (Kittelson & Associates, Inc., 2008).
Figure 1 The example of pre-emptive detection of EV
However, implementation using only traffic signal pre-emption is not enough to give adequate space for an EV to cross an intersection safely. Therefore, additional processes are necessary, such as accelerating the discharge of vehicles in front of the EV. Discharging congestion in front of EVs is crucial, especially if the road has only one lane.
Most experts have only briefly considered emptying vehicle congestion in front of EVs (Huang et al., 2011; Kamalanathsharma, 2010; Wang et al., 2013). Until now, studies have not examined means of promptly reducing lane traffic congestion in front of EVs. In 2011, the procedure proposed by Huang, Yang and Ma identified a need for accelerated discharging at a vulnerable position of congestion. However, the research did not develop a process for emptying. In 2010, Kamalanathsharma’s research only identified the time required by the last vehicle in the queue to reach the intersection. While this did provide a basis for determining the pre-emptive offset, again the research lacked further development of an emptying process. Likewise, the research conducted by Wang et al. (2013) predicted the time required for emptying the queue based on queue history, but their approach did not guarantee the avoidance of collision to a normal vehicle in front of the EV by the EV.
This research was aimed to develop a method and model for accelerated discharging of traffic in front of an EV at a single intersection. Accelerated discharging is the process of emptying a queue of normal vehicles immediately in front of the EV at the arm of the intersection. The method developed by Wang et al. (2013), is a pre-emptive extension system called TONGJI-Signal Emergency Vehicle Pre-emption System (TJ-EVSP) that can be applied to other pre-emptive systems. The proposed method and model of accelerated emptying presented here follows an historical approach. The tail of the queue path does not intersect with the trajectory of the EV across the intersection. The simulation test of the accelerated emptying model was conducted using MATLAB.
This paper proposed an accelerated emptying of lane traffic to facilitate access for EVs using an historical approach method that applied past characteristics of traffic. Using the proposed model, the emptying process can be analyzed using the defined system indicator. The results showed that, where the normal vehicle speed before accelerated emptying was the same as the EV speed, the system indicator was at zero percent, thereby indicating no requirement for accelerated emptying. The same results arose for the negative indicator. However, where the system indicator was close to one hundred percent, requirement for the accelerated emptying process was necessary.
Publication of the article was supported by the United States Agency for International Development (USAID) through the Sustainable Higher Education Research Alliance (SHERA) Program for Universitas Indonesia’s Scientific Modeling, Application, Research and Training for City-centered Innovation and Technology (SMART CITY) Project, Grant #AID-497-A-1600004, Sub-grant #IIE-00000078-UI-1.
Chakraborty, P.S., Nair, P., Sinha, P.R., Behera, I.K., 2014. Real Time Optimized Traffic Management Algorithm. International Journal of Computer Science & Information Technology (IJCSIT), Volume 6(4), pp. 119–136
EShanthini, E., Sreeja, G., 2016. Improved Traffic Control Systems for EV Clearance and Stolen Vehicle Detection. International Research Journal of Engineering and Technology (IRJET), Volume 3(3), pp. 630–635
Fuyu, W., Chunming, Y., Yanan, Z., Yan, L., 2014. Simulation Analysis and Improvement of the Vehicle Queuing System on Intersection Based on MATLAB. The Open Cybernetics & Systemics Journal. Volume 8, pp. 217–223
Goel, A., Ray, S., Chandra, N., 2012. Intelligent Traffic Light System to Prioritized Emergency Purpose Vehicles based on Wireless Sensor Network. International Journal of Computer Applications (0975-8887), Volume 40(12), pp. 36–39
Hashim, N.M.Z., Jaafar, A.S., Ali, N.A., Salahuddin, L., Mohamad, N.R., Ibrahim, M.A., 2013. Traffic Light Control System for EVs using Radio Frequency. IOSR Journal of Engineering (IOSRJEN), Volume 3(7), pp. 43–52
Hassn, H.A.H., Ismail, A., Borhan, M.N., Syamsunur, D., 2016. The Impact of Intelligent Transport System Quality: Drivers’ Acceptance Perspective. International Journal of Technology, Volume 7(4), pp. 553–561
He, Q., Head, K.L., Ding, J., 2011. Heuristic Algorithm for Priority Traffic Signal Control. Journal of the Transportation Research Board, Volume 2259(1), pp. 1–7
Huang, W., Yang, X., Ma, W., 2011. Signal Priority Control for EV Operation. In: 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, Leuven, Belgium, 22-24 June
Kim, J., Mahmassani, H.S., 2015. Spatial and Temporal Characterization of Travel Patterns in a Traffic Network using Vehicle Trajectories. Transportation Research Procedia, Volume 9, pp. 164–184
Kittelson & Associates, Inc., 2008. Traffic Signal Timing Manual. Federal Highway Administration, U.S. Department of Transportation
Shruthi, K.R., Vinodha, K., 2012. Priority Based Traffic Light Controller using Wireless Sensor Networks. International Journal of Electronics Signals and Systems (IJESS), Volume 1(4), pp. 58–61
Kamalanathsharma, R.K., 2010. Traffic Adaptive Offset-based Preemption for EVs. Master Thesis. Civil and Environmental Engineering, Virginia Polytechnic Institute, and State University, Alexandria
Kuang, X., Xu, L., 2012. Real Time Traffic Signal Intelligent Control with Transit Priority. Journal of Software, Volume 7(8), pp. 1738–1743
Law, A.M., Kelton, W.D., 2000. Simulation Modeling and Analysis. 3rd edition. USA: McGraw-Hill Higher Education, International Editions
Li, J., Pan, X., Wang, X., 2007. State-space Equations and First-phase Algorithm for Signal Control of Single Intersections. Tsinghua Science and Technology, Volume 12(2), pp. 231–235
Lim, S., 2016. Road Travel Time Prediction using Vehicular Network. Internetworking Indonesia Journal, Volume 8(1), pp. 5–9
Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A., 2016. A Hybrid Method for Short-term Traffic Congestion Forecasting using Genetic Algorithms and Cross Entropy. IEEE Transactions on Intelligent Transportation Systems, Volume 17(2), pp. 557–569
Loorbach, D., Shiroyama, H., 2016. The Challenge of Sustainable Urban Development and Transforming Cities. In: Governance of Urban Sustainability Transitions, Springer, Japan, pp. 3–12
Mannion, P., Duggan, J., Howley, E., 2016. An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control. In: Autonomic Road Transport Support Systems, Springer International Publishing, pp. 47–66
Ministry of Transportation, 2006. Minister of Transportation Decree No. 14 The year 2006 on the Management and Traffic Engineering on the Road. Available Online at http://hubdat.dephub.go.id/km/220-tahun-2006
Sumaryo, S., Halim, A., Ramli, K., 2014. Simulation and Analysis of Traffic Flow Models with EVs Distortion on a Single Road. In: International Conference on Technology, Informatics, Management, Engineering & Environment, Bandung, Indonesia, August 19-21
Sumaryo, S., Halim, A., Ramli, K., 2015. A New Modeling Approach for Queueing Vehicles in front of EV at a Traffic Intersection. International Journal of System Signal Control and Engineering Application, Volume 8(1), pp. 1–10
Sumaryo, S., 2016. Development of a New Model for Accelerated Traffic Discharging in front of the EV on an Intersection based on the Queueing Theory and Historical Data. Doctor Thesis, Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia
Ejidokun, T.O., Yesufu, T.K., Ayodele, K.P., Ogunseye, A.A., 2018. Implementation of an On-board Embedded System for Monitoring Drowsiness in Automobile Drivers. International Journal of Technology. Volume 9(4), pp. 819–827
Wang, Y., Wu, Z., Yang, X., Huang, L., 2013. Design and Implementation of an EV Signal Preemption System based on Cooperative Vehicle Infrastructure Technology. Advances in Mechanical Engineering, Volume 2013, pp. 1–10
Yang, S., Yang, X.Y, 2014. The Application of the Queuing Theory in the Traffic Flow of Intersection. International Journal of Mathematical Computational, Physical, Electrical and Computer Engineering, Volume 8(6), pp. 986–989