What Is The Difference In The Ticket Camera And The Intelligent Smart Traffic Light

Received 29 Feb 2020, Accepted 17 May 2020, Bachelor Online xvi June 2020.

1. INTRODUCTION

In city development, well infrastructure and direction of the roads will permit the citizens to travel efficiently from i place to another, especially during top hours, to improve the traveling experiences and savor safety traveling. Designing an intelligent traffic light arrangement (ITLS) that 100% fulfills the city's requirement is critically difficult for governors and stakeholders.

Traffic congestion is a serious issue, non just affecting the citizens, just likewise reducing the interests of the business activities. Although no statistics are reflecting the economic loss by traffic congestion in Hong Kong, but there are some proofs from the United States and European countries that traffic congestion inferred serious economic loss. In United states, a report released in 2014 [one] that US$124 billion was spent in a yr due to traffic congestion. In European Marriage, traffic congestion costs almost one% of its Gdp [2]. It shows that traffic congestion will lead to huge economical loss.

In addition to the economic upshot, traffic congestion volition harm man health. Although many vehicles are using environmental fuels, for example, liquefied petroleum gas that emits fewer pollutants, some public transport vehicles similar buses, trucks and other heavy vehicles are using diesel as the fuel, so nitrogen oxides and large amounts of particulates are emitted. These pollutants are trapped hands in urban because polluted air cannot be dissipated by many tall buildings and high usage of roads. Hence, poor air quality volition cause much illness, mainly in the respiratory arrangement. Charpin and Caillaud [3] proved that PM2.5, nitrogen oxides, and related pollutants caused the nasal allergic reaction to people, and ease to beal other respiratory diseases similar asthma, rhinosinusitis, and center ear disease. Also, mental disease such as traffic stress syndrome will be induced when people trapped in a narrow area with long traffic queues.

Traffic congestion is common to be seen on roads in Hong Kong. However, compared to other cities, the stock-still-wheel traffic light (FCTL) arrangement is currently in use for many years. Without considering the real-fourth dimension conditions of the road to maintain a suitable traffic arrangement, pedestrians and vehicles are usually in competitive status in using the cross side, and then dangerous situations are easy to occur considering both of them do non want to await in a junction. Hsieh and Y'all [4] proposed a scheme by applying an evolutionary algorithm in managing visitor routines to exhibition rooms in the museum. The scheme successfully realized the flow management in the museum to have the all-time feel for all visitors. Like to the traffic control, vehicles can exist considered every bit the visitors in entering the exhibition room, and intersections are the exhibition rooms that can perform the all-time traffic management. In reducing traffic congestion, an ITLS was developed by applying object detection and evolutionary algorithm. Classification and determination of the real-time situations captured on photographic camera set at the junctions were achieved by object detection. Data were analyzed and and then performed a strategic organization to the traffic lights by the evolutionary algorithm that aims to reduce the waiting time of vehicles and pedestrians at in intersection to increase efficiency of road usage.

2. LITERATURE REVIEW

2.1. Electric current Situation of Traffic Signal Control in Hong Kong

2.1.ane. Fixed-cycle traffic low-cal organization

The system simply uses a stock-still-cycle time in switching the traffic lights at each junction, which does non consider the real-time status of the corresponding intersection, such as the number of vehicles and pedestrians at the junction. And it does not take care of their needs, especially the waiting time and pass-through fourth dimension. Even the government carried out a research projection nearly extending the crossing time for the elderly [5], the results of the on-site trial may non reflect the actual traffic conditions since about of them are placed in quondam districts or under-saturated junctions.

2.ane.two. Area traffic control arrangement

Area Traffic Control (ATC) system has been applied to Hong Kong since 1977, which is the first computerized adaptive traffic point command organisation [6]. The arrangement deployed two dissimilar adaptive traffic control technologies, namely, Split Cycle Offset Optimization Technique (SCOOT) and Sydney Coordinated Adaptive Traffic System (SCATS) [7] in which traffic point was controlled by detecting the number of vehicles and computing their speed when they passed through the detector loops assail the vehicle lanes. The data are then transferred to the control server to adjust the parameters to the adaptive control algorithm that result to an optimal duration of green lights to release the vehicles, thus reducing traffic congestion. However, the system focuses on management of the vehicles passing through the junction, merely sacrifices the benefits of pedestrians that shortened the crossing time for pedestrians which increase the take a chance for people trying to cantankerous the road when the red light is on pedestrians. Besides, Ref. [7] stated that SCOOT and SCATS cannot provide rapid responses to traffic fluctuations at various intersections.

2.1.3. Smart traffic light system

Smart traffic light organization prepare up for Tai Tam Route (dam section) was developed and implemented by Cheng and his research team at The Chinese University of Hong Kong in the year 2018 [eight]. The type of vehicle will be detected by 8 sets of cameras installed at the street lamps on Tai Tam Road. Information technology will and then summate the green signal time, co-ordinate to the data received past all cameras analyzing the traffic queue of each side of the dam and the presence of heavy vehicles in the line resulting into an efficient calorie-free organisation to avert the traffic congestion at the dam because the pattern of the dam cannot hold 2 heavy vehicles like a charabanc charabanc and fire engine in parallel. This arrangement is a pioneer in Hong Kong applies video analytic technology by existent-fourth dimension traffic data collection to optimize traffic command. Information technology provides much valuable information to other researchers to develop an adaptive traffic control arrangement finer. On Tai Tam Road (dam department), there is a specific traffic condition, and fewer traffic situations occur due to lack of traffic control systems to perform the signal timing optimization at junctions in urban districts, especially in Central, Mong Kok, and Tsim Sha Tsui in Hong Kong. Based on the available open up data, such as street-facing surveillance cameras and navigation applications like the street view function in Google Maps, real-time detecting the number of vehicles and density of pedestrians in waiting or crossing the route at a specific intersection can be realized. This information describes the existent-fourth dimension traffic conditions of road intersections more than clearly, which could be a useful resource to bulldoze the development of intelligent traffic signal control for cities.

2.2. Review of Current Technologies

2.2.1. Object detection of car learning technology

From Ref. [8] that applied object detection to classify the type of vehicle when the vehicle passes through the checkpoints in Tai Tam Road, object detection is applicable in determining the blazon of object and also locating its position by quoting a bounding box effectually it. Furthermore, it also presents the information of the detected object in an image once classified [9]. Hence, object detection is well in use for detecting vehicles and pedestrians in the real scenario by setting up cameras at intersections.

2.ii.ii. Simulation

A simulation is an imitation of a model based on a system in reality. A simulation model can change dissimilar parameters, so perform testing for a system that is costly or hard to exist constructed. A simulation model tin can be used to investigate different states [10].

A simulation surround can be built to show the scenario past using the new traffic low-cal application, comparing with the current FCTL system. Moreover, it can simulate the operations in different traffic conditions by using the new application.

two.three. Review of Existing Solutions

Smith et al. [eleven] proposed a scalable urban traffic command system in 2013 that monitors the number of vehicles in real-fourth dimension by installing detectors at intersection by scanning the sensors installed in vehicles. The traffic information are gathered that transferred to traffic control to fix the scheduled scheme and so apply to intersections. Chavan et al. [12] proposed an intelligent traffic low-cal controller using an embedded system in 2009. The driver receives route suggestions as a reference to the destination by processing unit of measurement, which connecting with analyzing the traffic flow data came from sensors set of intersections. Nie [xiii] developed an intelligent traffic lights arrangement using object detection to discover out the number of vehicles at an intersection by cameras. Then fine-tune the traffic light switching by the results of the traffic density. Although those systems performed well, they gave up other vital elements like the precision that an incorrect vehicle queue was detected by a container truck. Also, they may be costly, especially in Ref. [11], that needs to install an actress component to vehicles which ways the system cannot be operated if vehicles pass through the road without installing sensors.

According to the quondam-styled applications that cannot satisfy the requirements in modern cities, Emami et al. [fourteen] suggested that machine learning technology is a manner to solve the problem. Applying to traffic control is increasing the efficiency in processing a large amount of data while data of traffic weather condition are sets of images. Furthermore, in using deep learning, more than precise information such as the type and speed of the vehicle tin be realized. Fuzzy expert systems are introduced past Hawi et al. [xv] in 2015 and they stated that the fuzzy logic controller proposed by Khiang et al. by implementing fuzzy logic started by Zadeh [sixteen]. Fahmy [17] presented with another arrangement called FLATSC included waiting time as a criterion in managing 4 intersection roundabout. Also, Hawi et al. also showed wireless sensor networks proposed by Yousef et al. [18] in 2010 that the traffic data are shared betwixt intersections to optimize the traffic catamenia. While fuzzy logic and wireless technologies get famous in solving this blazon of issue, the primal problem has not been resolved. That is, sensors are required to maintain the detection; hence much more costs volition be required based on the well-developed traffic network such equally Hong Kong. Furthermore, to bear the data sharing between intersections, and massive ability to support the data substitution is also needed.

While performing site testing may not be permitted in Hong Kong, simulation can perform different atmospheric condition virtually that will not touch the real traffic, specially in high traffic flow districts. SUMO [19] simulator is applied to generate the bodily scenario in Mong Kok. It creates the road map co-ordinate to the online maps. Then, the traffic flow is made and imported to the simulator. Although SUMO tin generate traffic flow very well, it cannot fulfill the needs of this paper. The traffic map cannot be modified one time created then that in that location is a barrier to making a modify to the route according to rapid constructions made or new facilities being implemented.

In summary, sensors are frequently used in unlike solutions in detecting traffic conditions. Nevertheless, limited data gathering and maintenance of these devices go complicated [13]. In improving traffic control, object detection technology becomes more than flexible and more practicable.

3. METHODOLOGY

iii.1. Overview of Methodology

This paper applies a build-examination methodology for the solution and evaluation pattern. For the solution design, the system is adult for an intelligent traffic low-cal to control the traffic light betoken automatically based on the machine learning with object recognition on the number of vehicles and pedestrians passing through a road intersection in Hong Kong.

For the evaluation design, the simulation demonstrated the comparison betwixt the current FCTL organisation and the proposed ITLS.

3.2. Design of the Solution

There were five components of the design shown in Effigy 1. Videos for different traffic conditions were taken at an intersection. The object detection awarding analyzed the video footage. The data of video footage analyzed were used for building traffic models. The traffic models were used to simulate the state of affairs and so that the current FCTL system and the proposed ITLS were implemented at the intersection. Finally, the simulation displayed traffic models visually.

three.3. Traffic Condition Videos

Videos for different traffic atmospheric condition were recorded as sources for the object detection application. Cameras were set at positions in which traffic flow of the intersection can be seen clearly. The footage was used for the object detection applications.

3.four. Object Detection Application

The system applies object detection engineering using Tensorflow [20] to clarify the video footage. As shown in Effigy two, the organisation tin recognize the object blazon appeared in the footage. It can be used to summate the number of vehicles and pedestrians virtually an intersection in a specific period of time.

three.5. FCTL Traffic Model

This traffic model simulated the traffic flow of the current FCTL at an intersection. After the simulation, the traffic model generated the average waiting fourth dimension of the pedestrians and vehicles at an intersection. The data were stored for algorithm evolution and testing purposes.

three.6. Intelligent Traffic Lite Traffic Model

The intersection was simulated with ITLS. The simulation was based on the number of pedestrians and vehicles estimated from the object detection application. The traffic light signal will be switched dynamically due to unlike traffic conditions. This traffic model aims to improve the efficiency of the traffic lights organization currently used in Hong Kong.

3.7. Simulation

The pygame application simulates the above traffic models visually. It used the data on traffic models to build a simulation that visualized the concept of the traffic light system as shown in Figure 3.

There were several features to improve the realistic situation of the traffic model:

The number of pedestrians and vehicles was prepare according to the output of the object detection application.
The traffic light betoken switching method was based on the analyzed information from the video footage.
The route map was fatigued according to the bodily intersection in Prince Edward in Kowloon, Hong Kong.
The speed of pedestrians and vehicles were false to real-life state of affairs. In that location was also a probability that a person will walk slower than average to simulate the motility of elderly.
Vehicle motility. The vehicle slowed downward if information technology is too shut to a vehicle in front of it. The vehicle stopped either the traffic light is red or enforced to stop while a barrier was detected, such as another vehicle or a pedestrian was detected at the front side of it.
There were dissimilar types of vehicles at the intersection.

3.8. Detailed Blueprint of Algorithms

The traffic light command algorithms were shown in Figures four and 5. Figure 4 shows the algorithm blueprint of the ITLS. Figure 5 shows the current FCTL system used in Hong Kong. The atmospheric condition determine the period of intelligent traffic calorie-free switching. The "threshold" in the figure were the parameter thresholds of ITLS. These parameter thresholds make up one's mind how the light signals were switched based on different situations.

The parameters thresholds were

carMaxNumAtJunction—The maximum number of vehicles at the junction.
carLightGreenMinTime—The minimum time of a vehicle light-green indicate should concluding.
carMaxWaitingtimeAtJunction—The maximum waiting fourth dimension for a vehicle at the junction.
pedMaxNumAtJunction—The maximum number of pedestrians at the junction.
pedLightGreenMinTime—The minimum duration time of the pedestrian green signal.
pedMaxWaitingtimeAtJunction—The maximum waiting time for a pedestrian at the junction.
pedLightFlashLongerTime—The time limit to extend the pedestrian flashing green point when at that place are still pedestrians crossing the street.

The default traffic light signal for vehicles and pedestrians was green and red, respectively. When the value of input parameters was higher than the threshold values, the traffic light signals will be triggered to modify. There was three seconds for the vehicle traffic low-cal to change from green to xanthous, and from yellow to red to allow the pedestrians cross the road by switching the red pedestrian light to green.

The cross-time for pedestrians was also based on the parameters. Earlier the pedestrian traffic light becomes blinking green, the system would keep checking if vehicle queue is less than the threshold or the number of waiting pedestrians are more than than the threshold, then the time of green pedestrian traffic light would be extended. If not, the pedestrian traffic light would change to blinking green.

While the green pedestrian traffic light is extended, the system would keep checking if the vehicle queue is more than the threshold, the pedestrian traffic low-cal would then modify to blinking green immediately and allow the vehicles move quickly.

Evolutionary algorithm is used to observe the all-time fix of parameter thresholds for the system to ameliorate the performance of the ITLS.

Before using the evolutionary algorithm, a regression model was built to predict the simulator effect based on the parameter set. Several simulation runs have generated the boilerplate waiting time of pedestrians and vehicles. The simulation data were used for training a regression model to predict the simulation output when the ITLS was implemented with unlike sets of parameter thresholds.

After the regression model was built, the evolutionary algorithm was used to find the optimal set of parameter thresholds in order to accomplish the best fitness with minimum average waiting time of pedestrians and vehicles.

The best parameter set by evolutionary algorithm will be determined past following steps:

Randomly generate a set up of parameter thresholds (as stated in Department 3.8) for vehicles and pedestrians every bit initial values.
Evaluate the fitness by the boilerplate waiting fourth dimension for vehicles and pedestrians using the parameter coefficient. Each coefficient is the weighting factor for the parameter while the value is set in the range according the actual conditions at the junction.
Sum upwardly all partial values of the waiting menstruation of the different pedestrians and vehicles to become the waiting time for vehicles and pedestrians.
Compare the new value with the current best value of the waiting time (fitness), then pick the parameter set with the goal of minimizing the fettle.
Repeat Steps 2 to four for various weather condition at the junction to perform the suitable fitness.

4. RESULT OF IMPLEMENTATION

The purpose of ITLS was to find a solution to alleviate traffic congestion in Hong Kong. The implementation effect of ITLS is presented in this section. It is used to evaluate the functioning of ITLS in improving the efficiency of route usage.

To evaluate the model, several traffic status videos were recorded, including non-height and superlative traffic periods. Each video recorded the traffic condition at the intersection between Prince Edward Road Due west and Beach Road in Hong Kong for one hr to perform the simulation.

The results were the comparing between the simulations that are using the ITLS for traffic control and the FCTL system. If the ITLS decreased the waiting time of cars and pedestrians at junctions, the efficiency of road infinite usage increased. The simulations using ITLS to manage the regular traffic hours were run many times. The results were stated in Sections 4.1 and 4.2.

4.1. Results of Non-peak Traffic Hours

In Hong Kong, non-pinnacle traffic hours are considered as the periods between 10:00 to 17:00 and 20:00 to 07:00 (the next twenty-four hours) in working days.

Tabular array ane shows the simulation results of 4 test cases with dissimilar time slots. By applying the ITLS at non-peak traffic hours, the average waiting time of all road users reduced from effectually l seconds to less than 30 seconds in all cases. It reflected that the efficiency in terms of the throughputs of the road can exist increased 44% to over threescore% by using ITLS. Examination cases 2 and three resulted in a lower increment of efficiency because case 2 falls in the dejeuner hour that there are probably more pedestrians and vehicles on the road. And the time is close to commuter time and cutting-off fourth dimension of trucks for cargoes delivery.

Overall Average Waiting Time (in Seconds)

Case	Time Slot	FCTL	ITLS	Efficiency Gained by ITLS (%)
1	x:thirty–xi:30	48	21	56.25
2	thirteen:00–14:00	47	25	48.89
three	16:00–17:00	45	25	44.44
4	20:30–21:xxx	53	twenty	62.26

Table 1

Simulation results on not-peak traffic hours.

4.2. Results of Elevation Traffic Hours

Peak hours are the periods between 07:00 to 10:00 and 17:00 to 20:00 in working days.

Table ii shows the simulation results of 4 peak traffic periods in working days. Since there are many vehicles and pedestrians waiting on the road intersection during meridian hours. ITLS profoundly reduced the total waiting fourth dimension of road users. The overall efficiency (in terms of reduction in full waiting time) has been increased 54% to 57% for all four cases. It reflected that the throughput at the intersection has been increased a lot.

Overall Boilerplate Waiting Time (in Seconds)

Case	Time Slot	FCTL	ITLS	Efficiency Gained by ITLS (%)
i	07:30–08:xxx	52	23	55.77
two	09:00–x:00	49	21	57.14
3	17:30–xviii:30	48	22	54.17
4	19:00–xx:00	50	22	56.00

Table 2

Simulation results on pinnacle traffic hours.

From the consequence generated by the evolutionary algorithm as shown in Effigy 6, there was a decreasing trend of the boilerplate waiting time for vehicles and pedestrians at the junction after each generation of the evolutionary algorithm. At that place was a decrease of 44% to over 60% of the total waiting time with the optimal set of parameter thresholds that fulfills the requirements, for both peak and non-meridian hours.

four.3. Optimal Prepare of Parameter Thresholds

With the employ of evolutionary algorithm, an optimal fix of parameter thresholds was found. The optimal gear up worked on both superlative and non-tiptop traffic hours. The results showed that fifty-fifty drivers or pedestrians were waiting in peak hours, the waiting period was close to the time in non-tiptop traffic hours. By the results, the system enhanced the efficiency when crossing the route or traffic calorie-free.

By using the optimal set of parameters, the average waiting time of drivers was about x seconds, and the boilerplate waiting time of pedestrians was approximately xiii seconds in not-peak traffic hours.

The average waiting time of drivers was about 10.81 seconds, and the average waiting time of pedestrians was approximately fourteen seconds in acme traffic hours.

According to the results imitation, the optimal set of parameter thresholds were determined every bit listed in Table 3.

Parameter	Value
Maximum number of vehicles at junction	12
Minimum time a car green indicate should last	21
Maximum waiting time for a car at junction	12
Maximum number of pedestrians at junction	11
Minimum time a pedestrian green point should last	forty
Maximum waiting time for a pedestrian at junction	twoscore
Time limit to extend the pedestrian flashing greenish betoken
when there are still pedestrians crossing the street	five

Table three

Optimal set of parameter thresholds.

5. EVALUATION

5.1. Evaluation of Object Detection Model

The dataset were divided into a training set and a testing gear up for the evaluation. Intersection over Union (IoU) was practical to determine the accuracy. We referred to the actual bounding box, and the predicted bounding box and calculated the overlapped surface area of the two bounding boxes. Tabular array 4 shows the related outcomes.

AP	Accuracy (%)
0.five IoU for vehicle	56.78
0.5 IoU for pedestrian	fifteen.48

Table 4

Average Precision(AP) of vehicle and pedestrian.

The average precision (AP) of the vehicle was 56% and for the pedestrian was fifteen%. The reason for AP of pedestrians resulting in low accurateness is that both the ground-truth and prediction bounding box are modest; therefore, they are challenged to have a high overlapping rate. Likewise, concerning more on the counting of objects rather than the IoU in implementation. The result meets the expectation for the ITLS with further improvements.

5.2. Evaluation of the Evolutionary Algorithm

The result of the evolutionary algorithm reduces the boilerplate waiting time of vehicles and pedestrians.

Figure 6 shows the results of each generation of the evolutionary algorithm. The average waiting time of pedestrians and vehicles were reduced by each generation. The average waiting time past combining the pedestrian and vehicle was about 65.92 seconds in the start generation. It became lower, with approximately 22.84 seconds in the tenth generation. The total average waiting time was reduced by 65% past using evolutionary algorithm in finding the optimal set of parameter thresholds.

The evolutionary algorithm initialized the parent parameter set and and so calculated the fettle of average waiting time of pedestrians and vehicles. Afterward that, the process determined the parameter gear up and calculated the eligibility of the parameter thresholds. When compared the current fitness with the all-time fitness value, the outcome optimized the choice of parameter set in each generation. The ITLS applied the optimal set up of parameter thresholds to model the traffic conditions in superlative 60 minutes and non-elevation hours in controlling the traffic catamenia.

6. Decision

In this paper, the ITLS based on auto learning with object detection and the evolutionary algorithm was presented. An object detection model was congenital to analyze traffic video footage. It outputted the number of vehicles and pedestrians in the video footage. A simulation was built for the comparing between the ITLS and the current FCTL system. Information technology included features to meliorate the realism of the simulation. Besides, the evolutionary algorithm was developed to optimize the parameter thresholds and command the best traffic light indicate switching method for the ITLS. The average waiting time of the pedestrians and the vehicles was used in finding the optimal set of thresholds by using evolutionary algorithm. With the utilise of ITLS, information technology was shown that the efficiency of the road usage has been improved, and hence the congestion problem can be alleviated.

Conflict OF INTEREST

No potential conflict of interest was reported by the authors.

AUTHORS' CONTRIBUTIONS

Ng designed and performed the experiments, derived the models and analysed the data. Kwok summarized the results and wrote the manuscript with the consultation of Ng.

Funding Statement

This project does not contain whatsoever funding supports.

ACKNOWLEDGMENTS

The authors would similar to express our great appreciation to Mr. Yui-Chung Fung, Mr. Chun-Yung So and Mr. Yuen-Ho Lam for collecting data and performing experiments of this inquiry works.

REFERENCES

1. F. Guerrini, Traffic congestion costs Americans $124 billion a twelvemonth, study says, Forbes, 2014.

3. D. Charpin and D.M. Caillaud, Air pollution and the nose in chronic respiratory disorders, C. Bachert, A. Bourdin, and P. Chanez (editors), The Nose and Sinuses in Respiratory Disorders (ERS Monograph), European Respiratory Society, Sheffield, UK, 2017, pp. 162-176.

7. A. Stevanovic, C. Kergaye, and P.T. Martin, SCOOT and SCATS: a closer look into their operations, in 88th TRB Annual Meeting, Transportation Research Board (Washington, DC, United states of america), 2009.

14. P. Emami, A. Rangarajan, S. Ranka, and Fifty. Elefteriadou, Machine learning for intelligent transportation systems, in UF Informatics Institute Student Data Analysis Seminar (Florida, USA), 2018.

18. K.K. Yousef, J.M. Al-Karaki, and A.Thousand. Shatnawi, Intelligent traffic light flow control organisation using wireless sensor networks, J. Inf. Sci. Eng., Vol. 26, 2010, pp. 753-768. http://www.iis.sinica.edu.tw/folio/jise/2010/201005_02.pdf

19. G. Behrisch, Fifty. Bieker, J. Erdmann, and D. Krajzewicz, SUMO-Simulation of urban mobility: an overview, Remember-Mind, in Proceedings of SIMUL 2011, The Third International Conference on Advances in Arrangement Simulation (Barcelona, Spain), 2011.

20. 1000. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, and M. Kudlur, Tensorflow: a system for big-calibration machine learning, in OSDI (Savannah, GA, USA), Vol. xvi, 2016, pp. 265-283.

Source: https://www.atlantis-press.com/journals/ijcis/125941268/view

Posted by: larsonourst1973.blogspot.com