Research Article | Open Access
Heng Yu, Yimin Wang, Feng Wang, Peiyun Qiu, "Understanding Impacts of Security Check on Passenger Flow in a Metro Station and Improving Measures: A Case Study in Guangzhou, China", Journal of Advanced Transportation, vol. 2019, Article ID 7438545, 9 pages, 2019. https://doi.org/10.1155/2019/7438545
Understanding Impacts of Security Check on Passenger Flow in a Metro Station and Improving Measures: A Case Study in Guangzhou, China
In order to ensure the safety of passengers using metro stations and staff working at them, some cities choose to set security checks at the entrances of metro stations. There is no doubt that security check can help keep dangerous objects out of a metro station. However, the security check can also slow down the entering speed of passenger flow and lead to congestions that may affect passengers’ travel plans. How security check will impact the passenger flow and how to reduce the impact are questions that need to be addressed. In this study, metro station models were constructed using the building structure and passenger flow data of a realistic metro station in Guangzhou, China. By using the AnyLogic simulation software, the traffic characteristics of passenger flow under the scenarios with and without a security check were compared and discussed. The congested areas in the station hall and possible causes were analyzed. In addition, possible improving measures such as adding security check machines and ticket vending machines were also modeled to test their effectiveness on reducing the congestion in the station. Results show that when security checks are set at each entrance of the station being studied, the flow rate of passengers entering the gate machine could be decreased by 49.4%~83.3%, which can cause serious congestion at the entrance during rush hours. By adding security check machines and ticket vending machines at the entrances with high passenger traffic demands, the congestion near these entrances could be greatly reduced.
In some cities with large population, metro system usually plays an important role in public transportation, and some metro stations can become very crowded in rush hours . However, there were a considerable amount of metro terrorist attacks happened in the metro stations around the world in the past decades [2, 3]. And some of them caused serious deaths or injuries [4, 5]. For security check can identify potential criminals and inhibit them from entering specific area , more and more cities begin to pay attention to security check for the big passenger flow and confined space makes it difficult to evacuate under emergency in a metro station. At the same time, new security technologies and inspect equipment are being developed . These phenomena are quite common, especially in China for metro systems in this country develop very fast and usually metro systems bear large passenger volume .
In this paper, a metro station with large passenger flow in Guangzhou, China, was chosen to be analyzed and studied. First, the passenger flow data in this station extracted from different dates were analyzed, and the amount of passengers entering the station during peak hours was determined. Then, based on its actual building structure and passenger flow data during peak hours, simulation models of passenger entering the station were established by using simulation software AnyLogic. According to the results of the simulation, the density maps of passenger in the station were analyzed and the congestion areas in the station hall were determined. Also models with security check and models with both security check and improving measures were calculated. The impact of security check on the flow of entering passenger and the effectiveness of the improving measures were also discussed.
2. Literature Review
Scholars have done some research about the security check and the passenger flow management of metro stations or other places. At first, considerations were mostly given to the security check and safety of airport. Considering the problem of airport congestion caused by the elimination of security screening, Wang et al. constructed an airport security game model to determine the optimal screening policy, which can maximize the reward from admitting normal applicants net of the penalty from admitting bad applicants . Bagchi and Paul analyzed the intelligence gathering of airport security check and the optimization for the resource allocation . Široký. J et al. established the queuing theory model of the check-in and security services for the airport terminals, and service level optimization method were also discussed .
Then in recent years, attentions have been paid to the passenger flow management, security of metro stations. By using simulation method, Huang et al. proposed a method combining simulation and evaluation to optimize the passenger flow in a metro station in Shenzhen, and the results showed it was a good way to support the passenger flow optimization . In order to help security design of metro systems, a frame was presented by Borrion et al.; security check, door frame metal detector, X-ray scanning, and other measures were included in this frame . Hu proposed a high-fidelity simulation method that integrates the strengths of microscopic pedestrian modeling and three-dimensional (3D) virtual reality for the evaluation of passenger flow organization and facility layout at metro stations, and by testing in a real metro station, he proved the operability of this method . An optimal screening policy was analyzed by Song et al., aiming to balance the security and the congestion problem caused by security check .
According to the literatures reviewed, there are just few literatures discussing the impact of security check in metro stations, for security check is not a mandatory measure in every metro station. However, with the growing passenger number and the frequent safety accidents, more and more metro stations begin to take measures to ensure the safety. Among the measures, checking the passengers’ luggage by X-ray machines or other kind of security check is most common. So how the security check will have influence on the passenger flow is becoming a new and important problem which needs to be evaluated and come up with solving measures.
3. Analysis of Passenger Flow of T Station
3.1. Introduction of T Station
T Station is located in the center area of Guangzhou downtown, and it is an interchange station for Line 1, Line 3A, and Line 3B of Guangzhou Metro. There are some large shopping malls, commercial streets, hospitals, and government agencies near T Station, which make T Station one of the busiest metro stations in Guangzhou. There are 3 layers in T Station; the facilities on each layer are shown in Table 1. Different layers of T Station are connected by steps or escalators.
There was no security check in Guangzhou Metro before 2008. During the 2008 Beijing Olympic and 2010 Guangzhou Asian Games, security check was needed before passengers enter each metro stations in Guangzhou; luggage brought by passengers must be checked by X-ray machine. The security check was cancelled after the games. In September 2015, Guangzhou Metro restarted security check. In most metro stations, handheld security check machines were adopted, and the security inspector with a handheld check machine will scan the bags when the passenger passes by. In August 2017, a more strict security check was carried out in Guangzhou Metro. In all stations, passengers must put their bags and other luggage into the X-ray check machines, also passengers themselves need to go through door frame metal detector to be checked. Figure 1 shows a typical security checkpoint in Guangzhou Metro.
It can be seen that the security check is becoming more and more strict in stations of Guangzhou Metro. With the increased occurrence of terrorist attacks at public venues, security check systems for subways have attracted more attention worldwide . On the one hand, a more strict security check will help find out the dangerous goods and deter potential attackers; whereas, it will also take the passengers more time to get into the station. This will increase travel time for passengers and also affect the efficiency of metro system. How will the security check influence the entrance passenger flow? How to find a balance between security check and the convenience of passengers? These two questions become important problems that need to be solved for metro stations preparing to add security check.
The layout of the station halls of T Station is shown in Figure 2, which looks like an inverted “T”. In the figure, TVM means ticket vending machine, GATE means gate machine, and the number in the bracelet means the number of the machines. There are 3 entrances for the station hall of Line 1; they are Entrance B, C, and D. The station hall of Line 3 has 4 entrances: they are Entrance A, E, G, and H. The locations and directions of escalators in the station hall are also marked in Figure 2. There are 7 groups of TVMs in the two station halls; they are marked as TVM1, TVM2…TVM7 in the figure. Also the gate machines which allow passengers to go to the inside area are marked as GATE1, GATE2…GATE5 and the escalators downward to platform are marked as ESC1, ESC2…ESC6 in Figure 2.
As mentioned before, T Station is located in the downtown of Guangzhou city; there are many shopping malls, commercial streets, and other buildings around it. The neighboring facilities which connect with each exit are shown in Table 2. The office workers, shoppers, and passengers transfer from other public transportations make T Station one of the most crowded stations in Guangzhou. The passenger contribution ratios of each entrance are listed in Table 2 according to on-site investigation.
3.2. Passenger Traffic Flow
The passenger flow data were extracted from the system of gate machines. Three different kinds of days are chosen; they are May 1, 2017(holiday), July 3, 2017(normal working day), and July 8, 2017(weekend). The number of passengers entering the station from each entrance in every hour during these three days is extracted. Figure 3 is drawn according to the passenger flow data; it shows the number change of people entering T Station every hour within one day. As shown in Figure 3, the change trends of passengers entering the station on May 1, 2017(holiday) and July 8, 2017(weekend) are almost the same. In these two days, the number of entering passengers keeps increasing before 16:00, and the peak number is about 14,000. After 16:00, the number of entering passengers begins to decrease until the station closes at 24:00.
However, there are two obvious peaks of the entering passengers’ amount on July 3, 2017 (normal working day). In morning rush hour, the number of entering passengers is about 4450. While in evening rush hour, the number of entering passenger is about 18,000. This means a total number of 300 passengers entering T Station from entrances every minute during the evening rush hour in normal working day.
According to the analysis above, it can be seen that whether on holiday, weekend, or workday, T Station is under high pressure caused by the large amount of entering passengers. There are 300 passengers entering the station every minute during the extreme rush hours. In such a situation, setting strict security check may cause serious queuing and crowding; thus, the efficiency of this metro station is influenced. Hence, this problem needs to be studied and solved.
4.1. Introduction of AnyLogic
AnyLogic is a simulation software that provides a graphical interface for modeling complex environments, such as manufacturing and logistics, healthcare, mining, stations, or road traffic. AnyLogic can also provide an environment where users can model these systems without conducting field experiments. Also, pedestrian movements are implemented by AnyLogic Pedestrian Library package which enables pedestrian agents move in continuous space, reacting on different kinds of obstacles (walls, different kinds of areas) and other pedestrians .
Blocks provided in the Pedestrian Library of AnyLogic can be used to construct flowcharts which simulate the movement of pedestrian. These blocks can be used to generate pedestrians, control pedestrian flow and process pedestrians; their symbols and functions are shown in Table 3 . So in this paper, AnyLogic was selected to establish the models, and the model was built based on the real dimension and layout of T Station as shown in Figure 2.
PedServices in pedestrian flow models define a group of similar physical service objects (ticket vending machines, security checkpoints, check-in counters, etc.). There are two types of service that can be used to simulate the physical services of pedestrian flow.(a)Service with Area: used to define service(s) with electronic queue (like in bank office, information office on the railway station, etc.). Pedestrians do not stand in a queue line but wait for their turn in the neighboring area.(b)Service with Lines: used to define service(s) with queue(s) where pedestrians wait in a queue line until the service becomes available. Two types of queues are supported: usual queue lines and “serpentine” queue typically used in airport check-in areas.
In the model of this paper, the function of PedServices is used to simulate passengers taking the security check, buying ticket, and crossing the gate machines. For passengers usually wait in a line to complete these steps, so service with lines is chosen to simulate the progress of passengers waiting to be checked, buying a ticket, and passing the gate machines.
4.2. Route Design in the Station Model
Passengers need to be checked before entering T Station. After that some of them will buy tickets, and then go to the gate machines. Some passengers holding the metro cards will go to the gate machines straightly. Then they will take escalators to go downward to the platform and board the metro trains.
As calculated in Section 3.2, in the extreme rush hour, the number of entering passengers is about 18,000, which means about 300 passengers enter the station every minute. For the entrances are also used as the exits at the same time, all the entrance tunnels in the model will be separated into 2 parts; entering passengers will use half part of the entrance tunnels only. The diameters of passenger are set to obey uninform (0.4, 0.5) m distribution, the initial speed of passengers obeys uniform (0.5, 1.8)m/s distribution, and the comfortable speed of passengers obeys uniform (0.8, 1.3)m/s distribution.
By counting the time it takes for each passenger to take the security check, security check time T1 in the model is set to obey the uniform distribution, which means T1~uniform(3.0, 5.0)second. According to the statistics of Guangzhou Metro about 65% passengers hold metro cards, so only 35% passengers will buy a ticket before they enter the gate machine. So in the model, 35% passengers entering from each entrance will buy a ticket after security check. The time T2 for a passenger to buy a ticket from the ticket machine is set to obey the normal distribution, that is T2~ normal(12.36, 42)second. Besides, the time T3 for each passenger to pass the gate machine is set to obey the exponential distribution, that is T3~ exponential (3.63)second.
Usually, after the security check, passengers from different entrance will try to use the TVM and gate machine which is closer to them, so the TVM and gate machine utilities by passengers from different entrance are designed and listed in Table 4. Passengers from some entrances can have multiple choices when using the TVM, gate machines, and escalators; the proportion of passengers who will use each machines is presumed. The passenger entering speed is also calculated according to the whole entering passenger number in extreme rush hours and the contribution ratios of each entrance are listed in Table 2.
Note: the percentage in the bracelet indicates the proportion of passengers entering this entrance will use this TVM or Gate machine.
Since the main consideration of this paper is the effect of security check on the entering passenger flow, Pedesink blocks are set at the location of downward escalators; this means the passengers will disappear here in the model and the movement of passengers after the escalators is not taken into consideration. Based on the information listed in Table 4, the routes of passengers entering from each entrance are constructed. The entering passengers’ route of Entrance E is shown in Figure 4. Passengers entering from other entrance will have similar route like Entrance E.
In the simulation models, the conditions in which there is entrance with and without security check are both considered. In the model without security check, passengers will buy tickets or use metro cards to go through the gate machines directly.
5. Results Analysis
According to the research of Oberhagemann (2012), when the crowd density is 5 persons/m2, there is still ample space between people to allow limited movement, and when the density is 6 persons/m2, individuals in the group can still move freely and still exert force on their neighbor by tilting. But the increased density makes the alternation of occurring force impossible because there is almost no room left for the individual to manoeuver . So the tolerable crowd density in the models is set to be 5 persons/m2 in this paper. When the crowd density at some places in the station hall is larger than 5 persons/m2, we say the station hall is overcrowded.
The crowd density under the conditions without and with security checks are shown in Figures 5 and 6, respectively. As can be seen from Figure 5, the flow of people entering from each station entrance can keep moving smoothly, and there is no congestion at the entrance when there are no security checks. The maximum density in this situation is about 4 persons/m2, which is not overcrowded according to the standard above. However, small congestions still appear in some areas, such as the areas near TVM2 and TVM3, for there are not enough TVM to serve the large number of passengers, and this situation needs to be improved.
As shown in Figure 6, when there are security checks set at each entrance, the congestion phenomenon appears at every entrance. The congestion is especially serious at the entrance with higher passenger entering speed, such as Entrance A, E, G, and H; the crowd density near these entrances can become as high as 7 persons/m2. Also it can be seen that a lot of passengers need to wait to get into the station when the security checks are added. So we can say that after the security checks are added at the entrances of T Station, the passenger flow will be blocked seriously.
By comparing the crowd density maps in Figures 5 and 6, it is obvious that the security checks at each entrance cannot meet the requirements of passenger flow in extreme peak hours. The security checks obviously made the passenger flow subject to a greater degree of blockage, thus affecting the speed of passengers entering the station. The number of passengers entering from each gate is collected from the calculated models with and without security checks, and they are shown in Table 5.
It can be seen that in the condition with security check at every entrances, the number of entering passengers decreases sharply. The number of entering passengers can decrease by 49.4 % ~83.3% in these gates. So it can be said that the security check can reduce the speed of entering passengers sharply.
In order to solve the congestion problem of T Station after security checks are set, improving measures listed below are added in the model with security check and calculated:(1)For entrances with serious congestion such as Entrance A, E, G, and H, the security checks machines are added to 2 for each entrance.(2)The numbers of machines of TVM2 and TVM3 are both added to 6. Also TVM6 is moved to the area near Entrance B, and 5 new TVMs are added near the entrance H, so passengers entering from each entrance can find TVMs more easily.
The results of model with both security checks and improving measures are shown in Figure 7 and Table 6. As shown in the crowd density map, after the improving measures were taken, the congestions at Entrance A, E, G, and H disappear, and also the congestion phenomenon near TVM2 and TVM3 is greatly eased. The average density at the entrance is about 2.67 persons/m2, which is much less than the density tolerance. Passengers can get into the station smoothly; no passengers are blocked outside the entrances.
The number of passengers entering each gate machine in 5 minutes was extracted from the model with both security check and improving measures; they are listed in Table 6. When compared with the passenger number n2, it can be found that the passenger number grows obviously when the improving measures are taken, especially for Gate 1, Gate 2, and Gate 3. The entering passengers from these 3 gates increased by 240%, 60.6%, and 60.4%, respectively, for the TVM and security check machines are added near these 3 gates. In general, the congestion phenomenon is greatly relieved after the improving measures added.
By establishing metro station models based on the building structure and passenger flow data of a real station, a method to evaluate the influence of security check on the entering passenger flow was introduced. Also the effectiveness of improving measures aiming to relieve the congestions was studied through model simulation. According to the analysis above, we make the following conclusions.(i)Establishing metro station models by simulation software AnyLogic is an effective way to evaluate the influence of security check. It can be used to make a judgment before making a decision to add security check to a metro station and find improving measures for entering passenger management.(ii)According to the simulation results, after security checks were added, the amount of passengers entering the gate machines can decrease by 49.4 %~83.3%. So it can be said that the security check can reduce the speed of entering passengers sharply.(iii)By adding security check machines and ticket vending machines at the entrance with large number of entering passengers, the congestions caused by security check can be greatly eased. The amounts of passenger entering the gate machines can be increased by 60%~240% when the improving measures above are taken near the area.
Some simplifications were made when establishing the model. First, passenger entering from each entrance will choose TVM and gate machines according to the designed routes in the model; actually some passengers may go around in the station hall to find the right way. Second, the possible conflicts of passenger flow getting in and out are not considered in this paper. These two factors can be modified in further research.
The models and data used in the paper are all available from the corresponding author upon reasonable request; the email address of the corresponding author is email@example.com.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
The authors would like to thank the Ministry of Science and Technology (P.R. China) for its funding (National Key R&D Program of China. No. 2016YFC0802500), and Mr. Heng Yu also want to thank China Scholarship Council for its financial aid.
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