sustainability

Article Optimization of Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in ,

Jiajie Yu, Yanjie Ji *, Liangpeng Gao and Qi Gao

Jiangsu Key Laboratory of Urban ITS, Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, , Nanjing 211189, China * Correspondence: [email protected]; Tel.: +86-138-1399-6939

 Received: 5 May 2019; Accepted: 2 July 2019; Published: 4 July 2019 

Abstract: Since the long dwell time and chaotic crowds make metro trips inefficient and dissatisfying, the importance of optimizing alighting and boarding processes has become more prominent. This paper focuses on the adjustment of passenger organizing modes. Using field data from the metro station in Nanjing, China, a micro-simulation model of alighting and boarding processes based on an improved social force paradigm was built to simulate the movement of passengers under different passenger organizing modes. Unit flow rate, delay, and social force work (SFW) jointly reflect the efficiency and, especially, the physical energy consumption of passengers under each mode. It was found that when passengers alighted and boarded by different doors, efficiency reached its optimal level which was 76.92% higher than the status quo of Nanjing, and the physical energy consumption was reduced by 16.30%. Both the findings and the model can provide support for passenger organizing in metro stations, and the concept of SFW can be applied to other scenes simulated by the social force model, such as evacuations of large-scale activities, to evaluate the physical energy consumption of people.

Keywords: alighting and boarding; social force model; passenger organizing; physical energy consumption; metro

1. Introduction

1.1. Background Since the mid-20th century, the excessive growth of the urban population has caused a series of problems, including traffic congestion and environmental pollution, which need to be urgently solved [1]. For many large cities with a large population, the fundamental solution for alleviating urban traffic congestion is to develop public transportation. Therefore, many cities have begun to build metro stations to carry such a large number of passengers. However, since the travel time on the metro has been getting longer due to the long dwell time, and passengers have to cross chaotic crowds to alight and board, the metro does need to improve its efficiency and level of service (LOS) and maximize the utilization of the space and infrastructure resources [2]. To achieve this, the travel time of metro passengers should be shortened, and it is necessary to organize passenger flow in a more appropriate way. The average speed of the metro and the total travel time of metro passengers on metro vehicles are greatly affected by dwell time. Yang’s (2017) study of metro timetables shows that the metro dwell time accounts for up to 24.37% of total travel time [3], so shortening the dwell time can improve the total efficiency of the metro to some extent. Improving the smoothness of passenger flow during

Sustainability 2019, 11, 3682; doi:10.3390/su11133682 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 3682 2 of 20 the alighting and boarding process can also improve the LOS of the metro and make passengers feel more comfortable.

1.2. Influencing Factors of Dwell Time Many researchers have found that dwell time consists of two parts: The time that it takes to open and close doors, and the time that the alighting and boarding process takes [4–6]. The time that it takes to open and close doors is an inherent property of the vehicle. With a certain vehicle, the time cannot be changed artificially, so it is necessary to shorten the time of the alighting and boarding process. There are many studies on the alighting and boarding process time. Previously, Levinson (1983) and Guenther et al. (1983) used a linear regression model and a natural logarithm model to describe the relationship between the alighting and boarding process time and the number of passengers [7,8]. They found that the number of passengers is a key factor that affects dwell time and that they are proportional to each other. After that, many other scholars also confirmed this in their studies [5,6,8] and found many other environmental factors that influence the alighting and boarding process. The type as well as the layout of the vehicle and the platform all have an effect on the alighting and boarding time [9–14]. In addition, the door width and the number of doors can also influence the alighting and boarding process time [5,10,15–20]. The alighting and boarding time of buses with four doors is 17% less than those with three doors, and the optimum door width is between 1.7 and 1.8 m. The horizontal and vertical gap between the doors and the platform can affect the alighting and boarding time [9,11,21–23]. The larger the gap, the longer is the alighting and boarding time, and Sebastian et al. (2015) also found that different sizes of the keep-out zone and passenger organizing modes can influence the time of that process [22]. These factors mentioned above all stem from the infrastructure of the metro station and the vehicle. In addition, the characteristics of passengers also affect alighting and boarding time consumption. The types of passengers, including their age, whether they are carrying luggage or not, and their density can influence alighting and boarding efficiency [24–26]; for example, younger passengers move faster than older people, and passengers with luggage need more time to alight and board than those without luggage. Moreover, some determinants of other aspects were found. For example, Fernandez et al. (2010) and Tirachini et al. (2011) claimed that if the payment process takes place outside the vehicle, the alighting and boarding time is shorter than if it takes place inside the vehicle [15,26]. There is an abundance of literature on the alighting and boarding process of public transportation. However, in existing studies, scholars have mainly studied the influence of the metro station environment, including the size of the facilities and the station layout and the characteristics of passenger traffic, such as group size, density, and distribution. In fact, the suggestions of these studies are not suitable for the existing metro stations due to the high cost of adjustment. Therefore, it is necessary to study the mechanism of passengers’ movement to explore a more feasible way to improve the status quo.

1.3. Analyzing Methods of the Alighting and Boarding Process Analyses of passenger traffic alighting and boarding processes mainly include regression analyses, simulations, real-scale experiments, and some other theories and techniques. Regression analyses are most often used to find the interrelations among several observable variables with plenty of field data, such as the studies mentioned above on the number of passengers and the alighting and boarding time [7,8,26]. Real-scale experiments are convenient because observers can record video data and organize passengers by different rules [15,20–22,27,28]. This method avoids the complex logical thinking and endless details involved in building a model. Real passengers can make the movement in experiments more reasonable and authentic. However, if a scene is hard to replicate or if the purpose is to study the mechanism of passengers’ movement, then a simulation can be an appropriate method. Many scholars have carried out passenger traffic simulation research. The simulation models are often divided into two parts, the macro and the micro. Henderson (1971) first measured the velocity Sustainability 2019, 11, 3682 3 of 20 distribution functions for three crowd fluids in the gaseous phase [29]. Hughes (2003) described passenger flow as a fluid with a certain mobility by using the concept of fluid mechanics and successfully simulated some phenomena of passenger traffic [30]. These were all macro-simulations of the alighting and boarding process. Since the end of the 20th century, many scholars have applied cellular automation in research on passenger traffic micro-simulation. Muramatsu et al. (1999) first applied the lattice gas model, a particular form of cellular automation, to passenger traffic simulation [31]. Blue et al. (2001) used the cellular automata model to simulate passenger traffic in different states, and the comparison with field data proved the availability and effectiveness of the model [32,33]. Zhang et al. (2008) improved the cellular automata model to make the simulation more suitable for metro passengers and highlighted the simulation scene and conditions in which the results were more accurate [34]. Lu (2017) used a cellular automaton to build a simulation of the alighting and boarding process to find out what influences the efficiency of this process [35]. Another micro-simulation, the social force model, is a type of continuous model, whereas cellular automaton is discrete. Helbing et al. (1995) first proposed the model and pointed out that the movement of passengers is a complex reaction resulting from various psychological forces including their own driving force [36]. They accomplished the building of a simulation model for moving crowds by using the social force model [37]. Based on an image-processing technology, passenger feature parameters were extracted from video images, and the parameters of the social force model were modified to make the simulation results closer to the field data [38,39]. Apel et al. (2004) solved the problem of low relative efficiency of the social force model calculation by using the Verlet link cell algorithm which integrates the equations of motion of about a thousand particles in a relatively easy way [40,41]. Lakoba et al. (2005) and Ding et al. (2011) improved the social force model and proposed the behavior rule to avoid an overlap between passengers, so that the model could reflect human behavior in a more authentic way. The simulation results accorded with the passenger traffic phenomenon [42,43]. By constructing the actual simulation scene, Rudloff et al. (2011) first proposed a simulation model of the alighting and boarding process in a metro station based on the social force model and applied the model to that process for the first time [44]. According to Mehdi et al. (2011), the desired velocity direction of an individual is the tangential direction of the path taking into account the existence of other individuals and obstacles, and the desired velocity of an individual does not exceed a certain fixed value [45]. Ji et al. (2018) inserted a direction rule into the social force model and combined the model with a multi-agent concept. They built a simulation for the alighting and boarding process of buses [14]. In addition, agent-based modelling was also used to simulate the alighting and boarding process, which can mitigate the risks of investment at a much lower cost and represents luggage as a different kind of an agent from passengers [12,25]. As for the simulation of the alighting and boarding process, most models focus on a scene with a single door and the corresponding platform. The auto-agents in the simulation are only alighting and boarding passengers. However, the alighting and boarding process is the passengers’ movement taking a vehicle carriage as a unit. That means that the alighting door and the boarding door of a specific passenger may be different, but in the same vehicle carriage, and different passenger organizing modes may need more than one door, while the inactive passengers who do not participate in the alighting and boarding process and stay in the vehicle or on the platform should also be taken into consideration. On the other hand, the analysis of the alighting and boarding process is only concerned with the efficiency of passenger flow in most studies. Subjective feelings are ignored due to the difficulty of quantifying this indicator. In view of the above limitations, an improved social force model was built to simulate the alighting and boarding process under different passenger organizing modes. The scene in this model was a whole vehicle and the corresponding platform which was not just a single door, and inactive passengers and their impact were also added to this model. Using field data from the metro station in Nanjing, China, the parameters in the model could be calibrated. In addition, based on the social force paradigm, Sustainability 2019, 11, 3682 4 of 20 this study quantified the physical energy consumption of passengers and the difficulty in finishing the alighting and boarding process by calculating the social force work (SFW). With the consideration of efficiency, a multi-objective optimization model was built, and we found that when passengers alighted and boarded by different doors, efficiency reached its optimal level, and the physical energy consumption was the lowest.

2. Model Description

2.1. Classic Social Force Model The social force model (SFM) is a multi-particle self-driven continuous micro-simulation model, the basic concept of which was proposed by Lewin (1951) [46]. After that, Helbing (1995) proposed the concept of the social force model and its calculation method [36]. Every auto-agent which represents the passenger in the simulation is subject to three kinds of forces which stem from the environment: The driving force and the forces from other passengers and obstacles [37]. The acceleration of the auto-agent is defined in Equation (1).

dvi(t) X X a = = f + f + f (1) dt id ij io j(,i) W

The change of position of the auto-agent i at the time t is showed in Equation (2).

dp (t) i = v (t) (2) dt i The three parts of the right-hand-side of Equation (1) correspond to the driving force and the forces caused by other passengers and obstacles. Each of these forces is described below. The driving force means the auto-agent’s eagerness to move towards the target. This force helps passengers to resist other forces which stem from their surroundings. The formula of the driving force is expressed in Equation (3). v0(t)e0(t) v (t) i i − i fid = (3) τi 0( ) where vi t refers to the desired speed of auto-agent i at time t. It is always an experience value and, of course, 0( ) can be calibrated in special situation. ei t is the desired direction of auto-agent i at time t. The desired direction of passengers will change according to the spatial position. In other words, the auto-agent has a different target in different movement stages, and its spatial distribution is shown in Figure1. vi(t) is the actual velocity of pedestrian i at time t. τi is the relaxation time which is always equal to 0.5 s. Sustainability 2019, 11, 3682 5 of 20 Sustainability 2019, 11, x FOR PEER REVIEW 5 of 21

Figure 1. DesiredFigure 1. directionDesired direction of alighting of alighting and boarding and boarding processes processes in ain metro a metro vehicle vehicle and and correspondingcorresponding platform. platform. In Figure1, the distance between each dotted line and door edge is the average radius of passengers. In Figure 1, the distance between each dotted line and door edge is the average radius of The desired direction outside of the dotted line points to the center of the door, while the desired passengers. The desired direction outside of the dotted line points to the center of the door, while the direction ofdesired the rest direction points of the to rest the points overall to the direction overall direction of the travel.of the travel. For alightingFor alighting passengers, passengers, the desiredthe desired direction direction can be be expressed expressed by Equation by Equation (4). (4). 𝒑(𝑀) −𝒑 (𝑡) 𝑊 𝑊  ,𝑝 >𝑦 𝑎𝑛𝑑 𝑝 <𝑥 − ,𝑜𝑟 𝑝 >𝑦 𝑎𝑛𝑑 𝑝 >𝑥 + p(M) pi(t) y x W y x W 𝒆 (𝑡) =‖𝒑(𝑀)− −𝒑(𝑡)‖ 2 2 + (4) 0  ( ) ( ) , pi > yM and pi < xM 2 , or pi >yM and pi > xM 2 ( ) =  p M(1,0p)i,𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒.t − ei t || − || (4)  (1, 0), otherwise. For boarding passengers, the desired direction can be expressed by Equation (5).

𝒑(𝑀) −𝒑(𝑡) 𝑊 𝑊 For boarding passengers, the,𝑝 desired <𝑦direction 𝑎𝑛𝑑 𝑝 <𝑥 can− be,𝑜𝑟 expressed 𝑝 >𝑦 𝑎𝑛𝑑 by 𝑝 Equation>𝑥 + (5). 𝒆 (𝑡) =‖𝒑(𝑀) −𝒑(𝑡)‖ 2 2 (5)  (−1,0),𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒. p(M) p (t) y y  − i x W x W The second part( ) of the( ) ,right-handp < yM-sideand ofp Equation< xM (1)2 is, theor pforc>esy causedM and by p other>xM passengers.+ 2 e0(t) =  p M pi t i i − i i (5) The formulai is shown− as follows:  ( 1, 0), otherwise. − 𝑟 −𝑑 𝒇 = 𝐴 exp 𝒏 +𝑘[Θ𝑟 −𝑑]𝒏 +𝜅[Θ𝑟 −𝑑]Δ𝑣𝒕 (6) The second part of the right-hand-side𝐵 of Equation (1) is the forces caused by other passengers. The formula is shown as follows: 𝑟 −𝑑,𝑟 ≥𝑑 Θ𝑟 −𝑑= (7) 0, 𝑟 <𝑑 ! rij dij h  i h  i − 𝑟 =𝑟 +𝑟 t (8) fij = Ai exp nij + k Θ rij dij nij + κ Θ rij dij ∆vjitij (6) Bi − − 𝑑 =𝒑 −𝒑 (9)

 𝒑 −𝒑  𝒏 =𝑛 ,𝑛r =d r d (10)  ij ij,𝑑 ij ij Θ rij dij =  − ≥ (7) −  𝒕 =−𝑛,𝑛 𝑜𝑟0, 𝑛,−𝑛rij< dij (11) Δ𝑣 =𝒗 −𝒗∙𝒕 rij = r i +rj (12) (8) where 𝐴,𝐵, 𝑘 and 𝜅 are positive constants. 𝑟 is the sum of the radius of passengers i and j. 𝑑 is d = p p the straight-line distance between passengersij i andi j.j 𝒏 is the direction from passenger j to (9) − passenger i. 𝒕 is the tangential direction of the link of the two passengers. Δ𝑣 is the difference   p p 1 2 i − j nij = nij, nij = (10) dij     t = n2 , n1 or n2 , n1 (11) ij − ij ij ij − ij   ∆vt = v v t (12) ji i − j · ij where Ai, Bi, k and κ are positive constants. rij is the sum of the radius of passengers i and j. dij is the straight-line distance between passengers i and j. nij is the direction from passenger j to passenger t i. tij is the tangential direction of the link of the two passengers. ∆vji is the difference between the Sustainability 2019, 11, 3682 6 of 20 tangential velocity of pedestrian i and j. The first part of the right-hand-side of Equation (6) is the psychological force of passenger i to avoid other passengers. The second part is the extrusion force between two passengers when they touch each other. The third part is the force perpendicular to the direction of the extrusion force, which is related to the tangential velocity difference. The forces which stem from obstacles are in the same form as those from passengers. The detailed formula is shown as follows: ! r d h  i h  i ij − io fij = Ai exp nij + k Θ rij dij nij + κ Θ rij dij (vi tio)tio (13) Bi − − ·

Based on all of the calculations above, the updated velocity of the auto-agent i is expressed by Equation (14). dvi(t) v (t + ∆t) = v (t) + ∆t (14) i i dt And the updated position is shown as Equation (15).

1 dvi(t) p (t + ∆t) = p (t) + v (t)∆t + ∆t2 (15) i i i 2 dt

2.2. Model Improvement

2.2.1. Definition of a Friction Surface Passengers who are focused on a target can only perceive impact within a very short distance. According to an existing study and field data [47], perceived distance is set as three meters, which means that passengers and obstacles inside a circle of an auto-agent with a radius of three m can affect the auto-agent, and the auto-agent will ignore the impact from those outside of the circle. The alighting and boarding process in a metro vehicle is a special movement process that includes two opposite directions of passengers, and the crowd density in this process is extremely high at peak hours, so that there exist intense and frequent collisions and squeezes among passengers in two directions. In addition, it was found that passengers moving in the same direction have a following effect, while passengers moving in the opposite direction rub against each other, and their difficulty in crossing doors increases [48]. In order to simulate this psychology of passengers, the concept of friction surface was proposed and integrated into the social force model. Friction surface is a physical concept that represents the surface of contact between two objects that are being rubbed against each other. In this paper, friction surface refers to the surface composed of passengers directly facing the opposite passengers, as shown by the yellow lines in Figure2. In the figure, the green points represent alighting passengers, and the orange points are boarding passengers. The gray ones are inactive passengers. Passengers on the friction surface have the responsibility of opening up a passage. The following passengers at the rear of the friction surface do not need to worry about opening up a passage. Instead, they choose to closely follow the passengers in front of them. In this way, the influence of the opposite passengers on the crowd behind the friction surface is weakened. In summary, passengers on the friction surface bear all of the friction forces from the opposite passengers, and passengers behind the friction surface do not. Sustainability 2019, 11, 3682 7 of 20 Sustainability 2019, 11, x FOR PEER REVIEW 7 of 21

FigureFigure 2. Friction 2. Friction surfaces surfaces in in the the alightingalighting and and boarding boarding process. process.

After determiningAfter determining the position the position of theof the friction friction surface,surface, the the social social forces forces of the ofpassengers the passengers are are calculatedcalculated according according to the to rules the rules mentioned mentioned above. above. The next next section section will willintroduce introduce the method the for method for checking whetherchecking passengerswhether passengers are in are a in position a position on on the the frictionfriction surface. surface. 2.2.2. Algorithm of the Friction Surface 2.2.2. Algorithm of the Friction Surface The friction surface is visible from any static state of passengers’ positions, but it cannot be The frictionrecognized surface by a computer. is visible The fromwork in any this staticsection stateis to make of passengers’ a computer recognize positions, the category but it of cannot be recognizedeach by apassenger. computer. The work in this section is to make a computer recognize the category of To check whether the auto-agent is on the friction surface, an evolutionary K-Nearest-neighbor each passenger.(KNN) algorithm is used to train the collected data. The KNN algorithm is a mature method in theory, To checkand whetherwas proposed the by auto-agent Cover and isHart on (1968) the friction [49]. Its surface,concept is anvery evolutionary simple and direct,K-Nearest-neighbor with the (KNN) algorithmadvantage is of used being to easy train to theoperate collected quickly data.and of Thehaving KNN a low algorithm error rate. The is a basic mature concept method of the in theory, and was proposedKNN algorithm by Cover is this: Calculate and Hart the (1968)distance [between49]. Its sample concept x and is each very training simple sample and according direct, with the to the distance function, select the nearest K samples to be the K nearest neighbors of x, and finally advantagedecide of being the category easy to of operate x accordin quicklyg to what and category of having most of a the low K nearest error rate.neighbors The belong basic to concept [50]. of the KNN algorithmIn this isstudy, this: sample Calculate x represented the distance the single between auto-agent sample who x andparticipated each training in the alighting sample and according to the distanceboarding function, process, select and the each nearest of themK sampleswent through to be the the processK nearest of becoming neighbors sample of x,x. andWhen finally the decide the categorynumber of x of according neighbors used to what for decidi categoryng the category most of of thesampleK nearestx is set as different neighbors values, belong the accuracy to [50 ]. In this of the decision changes accordingly, and the value of K with the highest accuracy is selected as the study, samplefinal number x represented of neighbors. the The single calculation auto-agent method whoof the participatedspecific evolutionary in the KNN alighting algorithm and is as boarding process, andfollows. each of them went through the process of becoming sample x. When the number of neighbors usedFor for alighting deciding passengers, the category of sample x is set as different values, the accuracy of the decision changes accordingly, and the value of K with1, 𝑒 the(𝑦) <0 highest accuracy is selected as the final 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 = (16) number of neighbors. The calculation method of the0, specific 𝑒 (𝑦) evolutionary>0 KNN algorithm is as follows.

For alighting passengers, 𝑑 = 𝑥 −𝑥 +𝑦 −𝑦 (17)  0( ) 1, ei y < 0 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 attribute = (16) refers to the walking directioni attribute0 of the passengers; passengers in the same 0, e (y)> 0 direction are marked as 1, while others are marked asi 0; 𝑒 (𝑦) is the value of the Y-coordinate of r  2  2 d = x x + y y (17) ij i − j i − j

attributei refers to the walking direction attribute of the passengers; passengers in the same 0( ) direction are marked as 1, while others are marked as 0; ei y is the value of the Y-coordinate of passenger i. The next step is to rank the distances dij between auto-agent i and other passengers from small to large and then to choose the first K passengers as neighbors.   j( ) = = neighbori k rank dij, j 1 to imax without i (18) Sustainability 2019, 11, 3682 8 of 20

Sustainability 2019, 11, x FOR PEER REVIEW 8 of 21 j neighbor (k) represents passenger j who is the kth nearest neighbor of pedestrian i. Next, check passengeri i. The next step is to rank the distances 𝑑 between auto-agent i and other passengers the attributionfrom small of toK largeneighbors. and then to choose the first K passengers as neighbors. 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟(𝑘) = 𝑟𝑎𝑛𝑘𝑑 , 𝑗 =1 𝑡𝑜 𝑖 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑖  (18) 1, i f attributek = 0, k = 1, 2, 3 ... K = 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟 (𝑘) f rictioni  (19) represents passenger0, j who is the kotherwiseth nearest .neighbor of pedestrian i. Next, check the attribution of K neighbors.

If the value of f riction is 1, that means1, 𝑖𝑓 that 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 passenger =0,𝑘=1,2,3…𝐾i is at a position on the friction surface. i 𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 = (19) If the value is 0, passenger i is not. If there0, are opposite 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒. passengers within the perceptual range of three m, theIf auto-agents the value of 𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 on the friction is 1, that surfacemeans that operate passenger under i is at normal a position social on the forces, friction whereas surface. for the other passengersIf the valuewho is 0, passenger are not on i is the not. friction If there surface, are opposite there passen is nogers force within from the the perceptual opposite range passengers. of three m, the auto-agents on the friction surface operate under normal social forces, whereas for the Takeother the passengers field video who data are asnot the on the experimental friction surface, sample there set.is no As force shown from the in theopposite black passengers. circle in Figure 2, the center ofTake the the circle field isvideo the data sample as the x experimental whose category sample is set. needed As shown to bein the decided, black circle and in theFigureK nearest neighbors2, the are center selected. of the Ifcircle there is the are sample one or x wh moreose thancategory one is oppositeneeded to be passengers decided, and among the K nearest the K nearest neighbors,neighbors then both are selected. the central If there one are and one the or oppositemore than one(s) one opposite are on passengers the friction among surface. the K Figure nearest3 shows the accuracyneighbors, of the then algorithm both the central when oneK andis setthe opposite at a diff one(s)erent are value. on the Thefriction calculation surface. Figure of the 3 shows accuracy is the accuracy of the algorithm when K is set at a different value. The calculation of the accuracy is as as follows: follows: NCP AccuracyK = 100%. (20) 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =TNP×× 100%. (20) The accuracy of a different K value is determined by the number of correct predictions (NCP) The accuracy of a different K value is determined by the number of correct predictions (NCP) and theand total the number total number of predictions of predictions (TNP). (TNP). The The experimentalexperimental samples samples are arepassengers passengers who can who be can be determineddetermined at the at friction the friction surface surface for for certain certain in the video. video. The The value value of NCP of NCPis the sum is the of sum𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 of .f rictioni. TNP is theTNP amount is the amount of experimental of experimental samples samples extracted extracted from the the video. video.

FigureFigure 3. Accuracy 3. Accuracy of theof the evolutionary evolutionary K-Nearest-neighbor (KNN) (KNN) algorithm. algorithm.

Figure3Figure shows 3 shows that whenthat whenK equals K equals 5 5 or or 6, 6,the the accuracy is ishigh high enough. enough. When When the value the of value K is of K is lower thanlower 5, than some 5, some of the of passengersthe passengers on on the the friction friction surface surface are are decided decided as not as being not being there, so there, the so the accuracyaccuracy is low. is On low. the On contrary, the contrary, when when the valuethe value of K ofis K more is more than than 6, someone6, someone that that is is not not on on thethe friction friction surface can also be placed into the wrong category. The accuracy is also low. In order to surfacereduce can also the beamount placed of calculation into the wrong in the codes, category. K = 5 is The set;accuracy that is, if there is also are low.opposite In orderpassengers to reduce in the amount of calculation in the codes, K = 5 is set; that is, if there are opposite passengers in the five nearest neighbors of the auto-agent, it can be decided that both of them are on the friction surface.

2.3. Definition of Indicators The optimization objective of different passenger organizing modes of the alighting and boarding process on the metro is not only to shorten time consumption; the consumption of passengers’ energy should also be taken into account. Therefore, in this paper, three indicators are proposed (social force Sustainability 2019, 11, 3682 9 of 20 work, average unit flow rate, and average delay) to measure the physical energy consumption and efficiency of passengers during the alighting and boarding process.

2.3.1. Social Force Work Every auto-agent in the simulation is subject to various forces from the environment and from other passengers. To complete the alighting and boarding process, the auto-agent is driven to overcome all of the impeding forces and move towards the goal by the driving force. During the moving process, the impeding forces do negative work on the auto-agent. In the case of the same displacement, the amount of negative work can be used to measure the difficulty of the completion of the process. The value of negative work is the consumption of energy [51]. Applying this concept to the alighting and boarding process, the value of negative work is the physical energy consumption of passengers. The magnitude of the negative work, or physical energy consumption, can be used to measure with how much difficulty these passengers complete the alighting and boarding process. When passengers feel relaxed when crossing the door, they need to overcome fewer impeding forces and do less social force work, and the physical energy consumption is lower. The formulation of SFW is shown below:

t=tmax X = Z i imax i PSF = F (t) vi(t)dt (21) i=1 SF · t=0

2 2 where P is the total social force work of the alighting and boarding process (m /s ped); tmax, imax SF · are the end time of the process and the number of the alighting and boarding passengers, respectively; i ( ) and FSF t is the social force that passenger i needs to resist at time t. In the process of simulation, the model outputs the total social force work of alighting and boarding passengers during the whole process. Under different crowd densities, the number of passengers changes a lot, so the total social force work has less value than the average social force work.

P avrg = SF PSF (22) imax

avrg where PSF is the average social force work. Next, the operation of social force work in the social force model will be explained. Social force work is the integral of force with respect to displacement. The integral of a function represents the area between the graph of the function and the X-axis. When x is divided into minimal segments, the integral can be equivalent to the sum of areas of several rectangles [52]. In the model in this study, the displacement during an iteration step is small enough as a minimal segment. The forces in the opposite direction of the desired direction are those that the passengers need to overcome. Multiplying the force of each minimal time segment by the displacement occurring in this small period, and completing the integral of the force with respect to the displacement of the whole moving process, gives us the social force work of the whole process.

2.3.2. Unit Flow Rate When the amount of alighting and boarding passengers is not the same, the total time of the alighting and boarding process does not have the value to measure the efficiency of the process. Therefore, the ratio between the total amount of alighting and boarding passengers and the time of this process, that is, the amount of passengers passing through a door in a unit time, is selected as the indicator of the speed of passengers in this process.

imax favrg = (23) tmax where favrg is the amount of passengers passing the door in unit time. Sustainability 2019, 11, x FOR PEER REVIEW 10 of 21

this process, that is, the amount of passengers passing through a door in a unit time, is selected as the Sustainabilityindicator2019 of, 11the, 3682 speed of passengers in this process. 10 of 20

𝑖 𝑓 = (23) 𝑡 2.3.3. Average Delay where 𝑓 is the amount of passengers passing the door in unit time. Boarding passengers need to wait outside the warning line. It is accepted that the space between the warning2.3.3. Average line andDelay the vehicle is named the door-space. When a passenger is in the door-space, it is necessary to find a gap to pass through the door to complete boarding or alighting. Under Boarding passengers need to wait outside the warning line. It is accepted that the space between differentthe warning passenger line organizing and the vehicle modes, is named the boarding the door-s orpace. alighting When speed a passenger of passengers is in the door-space, is different. it In the simulation,is necessary the averageto find a gap time to taken pass through by passengers the doorfrom to complete entering boarding the door-space or alighting. to Under leaving different the space is recorded,passenger which organizing represents modes, the average the boarding delay ofor thealig alightinghting speed and of boardingpassengers process. is different. In the simulation, the average time taken by passengers from entering the door-space to leaving the space Pi=imax is recorded, which represents the average delay of thei= 1alightingtdoori and boarding process. delaydoor = (24) imax ∑ 𝑡 𝑑𝑒𝑙𝑎𝑦 = (24) 𝑖 where delaydoor is the delay while passengers are in the door-space. t_doori is the time spent on passengerswhere 𝑑𝑒𝑙𝑎𝑦 passingthe is the door-space. delay while passengers are in the door-space. 𝑡_𝑑𝑜𝑜𝑟 is the time spent on passengers passing the door-space. 2.4. Model Operation 2.4. Model Operation In summary, the social force model describes that in the process of passengers moving towards In summary, the social force model describes that in the process of passengers moving towards their destination, other people and obstacles will have a psychological force of repulsion to the moving their destination, other people and obstacles will have a psychological force of repulsion to the pedestrian, thus generating a series of behaviors of avoiding obstacles and other people. Based on moving pedestrian, thus generating a series of behaviors of avoiding obstacles and other people. the modelBased descriptionon the model above, description the complete above, the simulation complete cansimulation be generated. can be generated. The operation The operation process of the modelprocess is shown of the in model Figure is 4shown. in Figure 4.

FigureFigure4. 4. ModelModel operation operation process. process.

3. Simulation Preparation

3.1. Data Collection This study took the station as the prototype for establishing the simulation. There are various types of metro stations, such as transfer stations, terminal stations, intermediate stations, and so on. However, in different types of metro stations, passengers move in the same way Sustainability 2019, 11, x FOR PEER REVIEW 11 of 21

3. Simulation Preparation

3.1. Data Collection Sustainability 2019, 11, 3682 11 of 20 This study took the Nanjing metro station as the prototype for establishing the simulation. There are various types of metro stations, such as transfer stations, terminal stations, intermediate stations, duringand the so alightingon. However, and in boarding different process, types of andmetro the stations, type of passengers station has move no influence in the same on way the behaviorsduring of alightingthe alighting and boarding. and boarding However, process, diff erentand the platform type of layouts,station has vehicle no influence types,and on the crowd behaviors densities of can affectalighting the experimental and boarding. results. However, Therefore, different the platform station layouts, where vehicle the data types, is collectedand crowd should densities be can typical, affect the experimental results. Therefore, the station where the data is collected should be typical, and during the collection, the crowd density should have a wide range of variations. and during the collection, the crowd density should have a wide range of variations. According to the above data collection requirements, Daxinggong metro station was selected for According to the above data collection requirements, Daxinggong metro station was selected for the fieldthe field observation. observation. Daxinggong Daxinggong station station isis oneone of the the busiest busiest metro metro stations stations in Nanjing, in Nanjing, China. China. It is It is a transfera transfer station station between between Line and 2 Lineand . The 3. The geographical geographical location, location, the the surroundings surroundings of of the the station, and thestation, environment and the environment inside the inside station the are station shown arein shown Figures in Figures5 and6 .5 and 6. The equipmentThe equipment used used to record to record videos videos was was HD SmartHD Smart Wireless Wireless Ip Camera Ip Camera XJB-DWF04 XJB-DWF04 (1080P,2.8 (1080P, MM). This is2.8 a MM). kind This of mini is a camerakind of mini and camera was installed and was high installed up on high the up wall. on the It iswall. tiny It enough is tiny enough that passengers that did notpassengers notice it, did and not it didnotice not it, interfere and it did with not theinterfer movemente with the of movement passengers, of inpassengers, which way in which the observation way errorthe was observation avoided. The error software was avoided. used toThe extract software data used from to the extract videos data was from Tracker the videos 5.0.6. was By using Tracker Tracker, 5.0.6. By using Tracker, the coordinate, velocity, and motion trail of each passenger could be extracted the coordinate, velocity, and motion trail of each passenger could be extracted from the videos. These data from the videos. These data were used to adjust the parameters in our model to make the simulation were used to adjust the parameters in our model to make the simulation close to reality. close to reality.

Sustainability 2019, 11, x FORFigureFigure PEER 5. REVIEW 5.Geographical Geographical locationlocation of of Daxinggong Daxinggong metro metro station. station. 12 of 21

(a) (b)

FigureFigure 6. Platform 6. Platform and and the the vehicle vehicle of of DaxinggongDaxinggong metro metro station: station: (a) (Environmenta) Environment of station of station platform; platform; (b) environment(b) environment of vehicle.of vehicle.

ThereThere are are five five doors doors on on each each side side ofof thethe vehicle on on Nanjing Nanjing metro metro Line Line 3, as 3, shown as shown in Figure in Figure 7. 7. To simplifyTo simplify the the scene, scene, only only one one side side of of thethe doorsdoors has been been drawn. drawn. The The green green areas areas are the are doors the doors of of the metro vehicle, and the yellow area is the warning line. Passengers are not allowed to step on the warning line while they are waiting outside the vehicle.

Figure 7. Schematic diagram of the simulation scene.

Passengers at this station were recorded from 18:30 to 19:30 on 17 December 2018, a period from the evening peak to the end of the peak hours on a weekday. Three doors were selected for continuous video recording. The video recorded the alighting and boarding process 84 times, including 2032 alighting and boarding passengers. Figure 8 shows the time consumption and unit flow of different amounts of passengers who completed the process. Figure 9 shows the arrival interval of the vehicle and the arrival rate of passengers. Sustainability 2019, 11, x FOR PEER REVIEW 12 of 21

(a) (b)

Figure 6. Platform and the vehicle of Daxinggong metro station: (a) Environment of station platform; (b) environment of vehicle. Sustainability 2019, 11, 3682 12 of 20 There are five doors on each side of the vehicle on Nanjing metro Line 3, as shown in Figure 7. To simplify the scene, only one side of the doors has been drawn. The green areas are the doors of the metro vehicle, and the yellow area is the warning line. Passengers are not allowed to step on the the metro vehicle, and the yellow area is the warning line. Passengers are not allowed to step on the warningwarning line whileline while they they are are waiting waiting outside outside the the vehicle. vehicle.

FigureFigure 7. 7.Schematic Schematic diagram diagram of the simulation simulation scene. scene.

PassengersPassengers at this at this station station were were recorded recorded from from 18:3018:30 to 19:30 19:30 on on 17 17 December December 2018, 2018, a period a period from from the eveningthe evening peak peak to the to the end end of theof the peak peak hours hours on on aa weekday. Three Three doors doors were were selected selected for continuous for continuous videovideo recording. recording. The The video video recorded recorded the the alighting alighting and boarding boarding process process 84 84times, times, including including 2032 2032 alightingalighting and and boarding boarding passengers. passengers. Figure Figure8 8shows shows thethe timetime consumption consumption and and unit unit flow flow of different of di fferent amounts of passengers who completed the process. Figure 9 shows the arrival interval of the vehicle Sustainabilityamounts of 2019 passengers, 11, x FOR PEER who REVIEW completed the process. Figure9 shows the arrival interval of the vehicle 13 of 21 and the arrival rate of passengers. Sustainabilityand the arrival 2019, 11 rate, x FOR ofpassengers. PEER REVIEW 13 of 21

(a) (b) (a) (b) Figure 8. Time consumption and unit flow rate of the alighting and boarding process of the field data: Figure(Figurea) Time 8. 8. TimeconsumptionTime consumption consumption of the and andalig unithting unit flow flow and rate rateboarding of of the the alighting process; alighting and(b and) Unit boarding boarding flow processrate process of the of of thealighting the field field data: data:and a b (boarding(a)) Time Time consumption consumptionprocess. of of the the alig alightinghting and and boarding boarding process; process; (b ( ) )Unit Unit flow flow rate rate of of the the alighting alighting and and boardingboarding process. process.

(a) (b) (a) (b) Figure 9. ArrivalArrival interval interval of of vehicles vehicles an andd arrival rate of passengers: ( (aa)) Arrival Arrival interval interval of of the the vehicle vehicle Figurefromfrom peak peak 9. Arrival hours intervalto off-peak off-peak of vehicles hours; ( ban) d Arrival arrival rate rate ofof of passengerspassengers passengers: fromfrom (a) Arrival peakpeak hourshours interval toto ooff-peak ffof-peak the vehicle hours.hours. from peak hours to off-peak hours; (b) Arrival rate of passengers from peak hours to off-peak hours. The findings show that the survey data were concentrated in medium and high crowd densities, whichThe is findingsin line with show the that purposes the survey of this data study, were to cooptimizencentrated passenger in medium organizing. and high The crowd arrival densities, interval whichof vehicles is in line in off-peakwith the purposeshours is ofsignificantly this study, tolarger optimize than passengerit in peak organizing. hours, and The the arrival arrival interval rate of ofpassengers vehicles inis smalleroff-peak in hours off-peak is significantlyhours than in larger peak hours.than it in peak hours, and the arrival rate of passengersThe parameters is smaller ofin theoff-peak simulation hours modelthan in were peak adjusted hours. according to the field data, and an error rangeThe of parameters15% was set of [18]. the Thesimulation calibration model of modelwere adjusted parameters according is judged to the by fieldrunning data, the and simulation an error rangemodel of several 15% was times set [18].and comparingThe calibration the simulationof model parameters results with is judgedactual investigationby running the results. simulation If the modeldeviation several from times actual and investigation comparing theresults simulation is within results the errorwith actualrange, investigationthe model parameters results. If theare deviationreasonable, from and actual the calibration investigation process results will is be within stopped. the errorThe model range, parameters the model areparameters determined. are reasonable,Otherwise, oneand wouldthe calibration need to adjustprocess the will internal be stopped. model parameters,The model rerunparameters the model, are determined. and modify Otherwise,the parameters one untilwould the need model to adjustresults the meet internal the error model range. parameters, rerun the model, and modify the parameters until the model results meet the error range. Sustainability 2019, 11, 3682 13 of 20

The findings show that the survey data were concentrated in medium and high crowd densities, which is in line with the purposes of this study, to optimize passenger organizing. The arrival interval of vehicles in off-peak hours is significantly larger than it in peak hours, and the arrival rate of passengers is smaller in off-peak hours than in peak hours. The parameters of the simulation model were adjusted according to the field data, and an error range of 15% was set [18]. The calibration of model parameters is judged by running the simulation model several times and comparing the simulation results with actual investigation results. If the deviation from actual investigation results is within the error range, the model parameters are reasonable, and the calibration process will be stopped. The model parameters are determined. Otherwise, one would need to adjust the internal model parameters, rerun the model, and modify the parameters until the model results meet the error range. 3.2.3.2. PassengerPassenger OrganizingOrganizing ModesModes 3.2. Passenger Organizing Modes MostMost ofof thethe existingexisting researchresearch onon thethe alightingalighting anandd boardingboarding processprocess inin metrometro stationsstations hashas focusedfocused onon aa sceneMostscene ofwithwith the aa existing singlesingle doordoor research andand the onthe the correspondingcorresponding alighting and platform.platform. boarding DueDue process toto thethe in limitationlimitation metro stations ofof thisthis has scene,scene, focused thethe influenceoninfluence a scene ofof with inactiveinactive a single passengerspassengers door and whowho the dodo corresponding notnot participateparticipate platform. inin thethe alightingalighting Due to andand the boarding limitationboarding processprocess of this isscene,is notnot takenthetaken influence intointo consideration,consideration, of inactive passengers andand thethe disappearancedisappearance who do not participate ofof passengerspassengers in the after alightingafter crossingcrossing and boarding thethe doordoor process isis notnot takentaken is not intotakeninto consideration,consideration, into consideration, either.either. and ToTo remedy theremedy disappearance thisthis probleproble ofm,m, passengers thisthis studystudy afterexpandedexpanded crossing thethe the simulationsimulation door is not scenescene taken fromfrom into aa singleconsideration,single doordoor toto either. anan entireentire To remedy vehicle,vehicle, this andand problem, addedadded thisthethe studyinactiveinactive expanded passengers.passengers. the simulation ThisThis representedrepresented scene from thethe a entire singleentire alightingdooralighting to an andand entire boardingboarding vehicle, processprocess and added inin aa moremore the inactive comprehensivecomprehensive passengers. way.way. This represented the entire alighting and boardingTheThe simulationsimulation process in ascenescene more waswas comprehensive thethe samesame asas way. thatthat inin LineLine 33 ofof thethe NanjingNanjing memetro.tro. PassengersPassengers werewere generatedgeneratedThe simulation randomlyrandomly scene toto queuequeue was the aroundaround same asthethe that doordoor in Lineandand 3werewere of the controlledcontrolled Nanjing metro. byby differentdifferent Passengers rulesrules were ofof passengerpassenger generated organizingrandomlyorganizing to modes.modes. queue around TheThe threethree the doordifferentdifferent and were modesmodes controlled thatthat passengerspassengers by different cancan rules bebe oforganizedorganized passenger intointo organizing areare shownshown modes. asas follows:Thefollows: three different modes that passengers can be organized into are shown as follows: 1. Scenario 1: passengers alight through the middle of each door while boarding on the two sides 1. ScenarioScenario 1: passengers alight throughthrough thethe middlemiddle of of each each door door while while boarding boarding on on the the two two sides sides of of the door, which is shown in Figure 10. ofthe the door, door, which which is shownis shown in in Figure Figure 10 .10.

Figure 10. Passenger organizing in scenario 1. Figure 10. Passenger organizing in scenario 1.

2.2. ScenarioScenarioScenario 2:2: 2: passengerspassengers passengers alightalight alight fromfrom from thethe the leftleft left sideside side ofof of eacheach doordoor whilewhile boardingboarding onon thethe right right side side of of thethethe door,door, door, whichwhich isis shownshown inin FigureFigure 11.1111..

FigureFigure 11. 11. PassengerPassenger organizing organizing in in scenario scenario 2. 2. Figure 11. Passenger organizing in scenario 2.

3.3. ScenarioScenario 3:3: passengerspassengers alightalight fromfrom differentdifferent doorsdoors thanthan thosethose usedused forfor boarding,boarding, whichwhich isis shownshown inin FigureFigure 12.12.

Sustainability 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/sustainability Sustainability 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/sustainability Sustainability 2019, 11, 3682 14 of 20

3. Scenario 3: passengers alight from different doors than those used for boarding, which is shown Sustainabilityin Figure 2019, 1211,. x FOR PEER REVIEW 2 of 21

Figure 12. Passenger organizing in scenario 3. Figure 12. Passenger organizing in scenario 3.

The resistance resistance of of the the passengers passengers in in the the boarding boarding and and alighting alighting process process comes comes from from two two parts parts at least:at least: • Passengers need to compete for the doorway to pass through the doors.doors. • • Passengers need need to to compete for space to settle down in the vehicle or platformplatform after passing • through the the doors. doors.

Due toto thethe complexity complexity of resistanceof resistance factors, factors, which which scenario scenario is optimal is optimal was not obvious.was not Therefore,obvious. Therefore,micro-simulation micro-simulation and indicators and wereindicators needed were to assistneeded the to analysis. assist the analysis. Crowd density influences influences the alighting and boar boardingding process, such as whether the inactive passengers in the vehicle and on the platform can avoid others in time, and whether alighting and boarding passengers can can find find a a gap to cross the door in a short time. In the same scenario, different different crowd densitiesdensities were were compared. compared. The The study study compared comp theared physical the physical energy consumptionenergy consumption and efficiency and efficiencyunder these under different these conditions different conditions and found and the mostfound suitable the most passenger suitable passenger organizing organizing mode. mode. According toto the the field field survey, survey, during during evening evenin peakg hours,peak thehours, proportion the prop of alightingortion of passengers, alighting passengers,boarding passengers, boarding passengers, and inactive and passengers inactive passengers is approximately is approximately 3:3:4, respectively. 3:3:4, respectively. In the following In the followingsimulation, simulation, passengers’ passengers’ generation ge wasneration based was on fieldbased data. on field data.

4. Results Results and and Discussion Discussion First, it it is is necessary necessary to to make make the the differences differences among among the the three three passenger passenger organizing organizing modes modes clear. clear. Table1 1 showsshows aa comparison of the number number of of friction friction surfaces, surfaces, unit unit flow flow rates, rates, and and average average delays delays in thesein these scenarios. scenarios.

Table 1. Overall comparison of each scenario.

NumberNumber of ofFriction Friction Surfaces Surfaces Unit Unit Flow Flow Rate (peds(peds/s)/s) Average Delay Delay (s) (s) ScenarioScenario 1 1 2 2 3.53 2.24 2.24 ScenarioScenario 2 2 1 1 4.00 2.10 2.10 ScenarioScenario 3 3 0 0 4.93 1.84 1.84

The findings findings show that as the number of frictionfriction surfaces increases, the average delay also increases.increases. When When the the number number of of friction friction surfaces surfaces pe perr door door increased increased from from 0 0 to to 1, the delay increased by 14.13%. When When the the number number of of friction friction surfaces surfaces in increasedcreased from from 1 1 to to 2, 2, the the delay delay increased increased by by 6.67%. 6.67%. Also, the unit flow flow rate decreased by 18.86% and 11.75% with the number of friction surfaces adding up from 0 to 1 and 1 to 2. respectively. When thethe crowdcrowd density density is atis diatff differenterent levels, levels, the trendsthe trends of the of three the indicators three indicators are clearly are di clearlyfferent, different,and thus, theand three thus, indicators the three at indicators different crowd at diff densityerent crowd levels shoulddensity be levels analyzed should separately. be analyzed In the separately.simulation, In the the relative simulation, crowd the density relative of crowd 1 represented density of the 1 extremelyrepresented empty the extremely metro station, empty and metro the station, and the relative density of 7 represented the extremely crowded situation. Next, we divided all the density levels into three parts, and defined 1–3 as low density, 3–5 as medium density, and 5– 7 as high density. Sustainability 2019, 11, 3682 15 of 20

relative density of 7 represented the extremely crowded situation. Next, we divided all the density levelsAs into can three be seen parts, in and Table defined 2, the 1–3 study as low found density, that 3–5 at asall medium three levels, density, delay and increased 5–7 as high with density. the increaseAs canin crowd be seen density. in Table The2, the existence study found of friction that atsu allrfaces three had levels, a significant delay increased impact on with the the traffic increase rate inin the crowd crowded density. state, The and existence the higher of friction the number surfaces of friction had a significant surfaces, the impact higher on the traimpedingffic rate forces in the atcrowded every density state, and level the were. higher With the density number increasing of friction from surfaces, the low the higherlevel to the the impeding high one, forces the unit at everyflow ratedensity in Scenario level were. 2 was With 20.32%, density 10.05%, increasing and 11.81% from la therger low than level it was to in the the high current one, scenario the unit (Scenario flow rate 1),in Scenariorespectively. 2 was In 20.32%, Scenario 10.05%, 3, the and corresponding 11.81% larger thanincrement it was was in the 77.46%, current 41.80%, scenario and (Scenario 28.30%, 1), respectively.respectively. As In Scenariofor the average 3, the corresponding delay, in Scenario increment 2 it was was 15.28%, 77.46%, 8.52%, 41.80%, and and −10.00% 28.30%, larger respectively. than it wasAs for in the averagecurrent scenario delay, in with Scenario the density 2 it was increa 15.28%,sing. 8.52%, In Scenario and 10.00% 3, the delay larger was than 6.25%, it was 0, in and the − 23.46%current smaller scenario than with the the current density scenario. increasing. Although In Scenario efficiency 3, the delay in Scenario was 6.25%, 3 was 0, andprominently 23.46% smaller high, therethan thewas current a strange scenario. phenomenon Although in ethefficiency analysis in Scenario above. The 3 was unit prominently flow rate in high, Scenario there was2 was a strangehigher thanphenomenon that in the in current the analysis scenario above. with The any unit density, flow rate but in the Scenario delay in 2 wasScenario higher 2 was than larger that in than the currentthat in thescenario current with scenario any density, when butthe thedensity delay level in Scenario was low 2 wasand largermid. Also, than according that in the to current Figure scenario 13, it can when be observedthe density that level the wasdelay low in Scenar and mid.io 2 Also,was extremely according unstable. to Figure 13, it can be observed that the delay in Scenario 2 was extremely unstable. Table 2. Indicators in different levels of density. Table 2. Indicators in different levels of density. Low Density Mid Density High Density Scenario 1 Unit flow rate (peds/s) Mean Low 3.15 Density Mid 3.78 Density High 3.64 Density

Scenario 1 Unit flow rate (peds/s)S.D. Mean 0.23 3.15 0.28 3.78 0.40 3.64 Delay (s) MeanS.D. 1.44 0.23 1.76 0.28 2.60 0.40 Delay (s)S.D. Mean 0.34 1.44 0.14 1.76 0.61 2.60 S.D. 0.34 0.14 0.61 Scenario 2 Unit flow rate (peds/s) Mean 3.79 4.16 4.07 Scenario 2 Unit flow rate (peds/s) Mean 3.79 4.16 4.07 S.D.S.D. 0.42 0.42 0.37 0.37 0.41 0.41

DelayDelay (s) (s) Mean Mean 1.66 1.66 1.91 1.91 2.34 2.34 S.D.S.D. 0.33 0.33 0.33 0.33 0.35 0.35 ScenarioScenario 3 3 Unit Unit flow flow rate rate (peds/s) (peds/s) Mean Mean 5.59 5.59 5.36 5.36 4.67 4.67 S.D. 0.39 0.62 0.33 S.D. 0.39 0.62 0.33 Delay (s) Mean 1.35 1.76 1.99 Delay (s) MeanS.D. 1.35 0.38 1.76 0.18 1.99 0.19 S.D. 0.38 0.18 0.19

(a) (b)

FigureFigure 13. 13. UnitUnit flow flow rate rate and and delay delay at at different different density density levels: levels: ( (aa)) Unit Unit flow flow of of each each scenario; scenario; ( (bb)) delay delay ofof each each scenario. scenario.

In this paper, the difference between the competitiveness of passengers in the two directions is called competitiveness gap, which is shown in Table3. In Scenario 2, the competitiveness gap was 0. That means that boarding passengers had the same competitiveness as alighting passengers.

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Sustainability 2019, 11, 3682 16 of 20 Table 3. Comparison of the competitiveness gap in each scenario.

Scenario 3 2 1 The competition for space of these passengers was fiercer than it was in other scenarios where the competitiveness gap wasNumber larger. This of the may friction have causedsurfaces the 0 instability 1 in2 Scenario 2. Competitiveness gap 1 0 n∈(0,1) Table 3. Comparison of the competitiveness gap in each scenario. In this paper, the difference between the competitiveness of passengers in the two directions is called competitiveness gap, which is Scenarioshown in Table 3. In Scenario 3 2 2, the 1 competitiveness gap was 0. That means that boardingNumber passengers of the had friction the surfacessame competitiveness 0 1 2as alighting passengers. The Competitiveness gap 1 0 n (0, 1) competition for space of these passengers was fiercer than it was∈ in other scenarios where the competitiveness gap was larger. This may have caused the instability in Scenario 2. InIn addition addition to to the the unit unit flow flow rate rate and and delay, delay, th thereere were were clear clear differences differences in in the the physical physical energy energy consumptionconsumption among among the the three three scenarios. scenarios. Figure Figure 14 14 shows shows the the comparison comparison of the of the trends trends of SFW of SFW in differentin different scenarios. scenarios. The The SFW SFW of Scenario of Scenario 1 was 1 was sele selectedcted as the as theunit unit value, value, and and the theratios ratios of SFW of SFW of Scenariosof Scenarios 2 and 2 and 3 to 3 toScenario Scenario 1 1were were taken taken as as th thee measurement measurement indicators indicators to to obtain obtain the the visual visual comparisoncomparison chart chart of of SFW SFW in in the the three three scenarios. scenarios.

Figure 14. Comparison of social force work (SFW) in different scenarios. Figure 14. Comparison of social force work (SFW) in different scenarios. When crowd density was at a low level, space resources were sufficient. The competition between passengersWhen goingcrowd in density different was directions at a low is not level, obvious. space With resources crowd were density sufficient. increasing, The the competition competition betweengradually passengers became more going intense. in differ Thisent directions phenomenon is not is verifiedobvious. in With Figure crowd 14. density In Scenario increasing, 2, the SFW the competitionwas clearly largergradually than became it was more in the intense. current This scenario phen atomenon the medium is verified and in high Figure densities. 14. In Scenario Although 2, thethe SFW efficiency was clearly of the boardinglarger than and it alightingwas in the process current was scenario higher at than the it medium was in the and current high densities. situation, Althoughthe consumption the efficiency of SFW of was the stillboarding higher and than alighting the current process level. was That higher means than that it thewas physical in the current energy situation, the consumption of SFW was still higher than the current level. That means that the consumption was the highest among the three scenarios in mid and high density in Scenario 2. Similarly, physical energy consumption was the highest among the three scenarios in mid and high density in in Scenario 3, there was no space competition between the two different directions of passengers; Scenario 2. Similarly, in Scenario 3, there was no space competition between the two different there was only competition between the alighting or boarding passengers and the inactive passengers directions of passengers; there was only competition between the alighting or boarding passengers Thus, the SFW or physical energy consumption of Scenario 3 in a medium crowd density was lower and the inactive passengers Thus, the SFW or physical energy consumption of Scenario 3 in a medium than that of other scenarios, as in the low crowd density. Therefore, the physical energy consumption of crowd density was lower than that of other scenarios, as in the low crowd density. Therefore, the passengers was the lowest in Scenario 3, especially in the low and mid density. Although the efficiency physical energy consumption of passengers was the lowest in Scenario 3, especially in the low and in Scenario 2 was larger than that in Scenario 1, the physical energy consumption was too large in mid and high crowd density.

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When the operators organize the passenger flow, it is necessary to make the emphasis of each situation clear. For example, in some special vehicles, like the women’s vehicle in Guangzhou, China, only women and children can get on. In this situation, the physical energy consumption is more important than efficiency. Based on such emphasis, the optimal measure can be determined. This paper has made an adjustment and an improvement to the existing social force model. Using field data from the Nanjing metro station, the study performed a simulation of passenger flow over the whole metro vehicle and the corresponding platform. It simulated the alighting and boarding process under different passenger organizing modes. The delay, unit flow rate, and social force work jointly determine the efficiency of the alighting and boarding process and physical energy consumption of passengers under different passenger organizing modes. The comparison of different modes showed that when passengers alighted and boarded by different doors, efficiency reached its optimal level, and the physical energy consumption was the lowest. It was also found that, sometimes, a more efficient scenario may not be the more energy-saving one. When making decisions, operators need to make choices according to where emphasis is placed. In the process of model building, to describe the phenomenon of passengers moving in the same direction having a following effect, the KNN algorithm was used to determine the friction surface. If there is an opposite pedestrian among the five nearest neighbors of the auto-agent, it is judged that both of them are at a position on the friction surface. Passengers at a position on a friction surface can feel the force from others going in the opposite direction. Through the simulation in this study, the efficiency and the physical energy consumption of the alighting and boarding process under different passenger organizing modes were analyzed. Compared with existing studies, the contribution of this paper is mainly reflected in these three aspects:

(1) A full scenario was simulated both in the environment mock-up and the auto-agent generation. The simulation scenario was expanded from a single door to a whole vehicle and the corresponding platform, and inactive passengers who do not participate in the alighting and boarding process were added. This made the simulation more integrated, and the needs of different passenger organizing modes could also be met. (2) The existing social force model was improved by differentiating between passengers’ positions. They were divided into two groups: On friction surfaces or not. Passengers in different groups follow different rules to move. In this way, the existing social force model was improved in terms of both rationality and accuracy. (3) In the process of analyzing the simulation results, social force work was used to quantify the subjective feelings of the physical energy consumption and the difficulty degree in completing the alighting and boarding process, so as to establish a more comprehensive evaluation index system for the evaluation of the efficiency and LOS of passenger organizing.

The evolution of public transport is similar from mode to mode. Concerning bus services in China, single-door boarding and alighting has been improved by more efficient front-door boarding and back-door alighting. During the transition process, bus passengers were guided by conductors’ instructions and voice broadcasting. Then, they gradually accepted this management measure. For another example, the metro in Guangzhou, one of the largest cities in China, has already applied different passenger organizing modes at the same time, including Scenarios 1 and 2 (shown in Figure 15) in this study, and some other modes unmentioned in this paper. Passengers can follow the instructions of different modes very well. Hence, with the appropriate guidance signs and administrators’ instructions, the adjustment of passenger organizing will be accepted smoothly. Sustainability 2019, 11, 3682 18 of 20 Sustainability 2019, 11, x FOR PEER REVIEW 3 of 21

(a) (b)

FigureFigure 15. 15. ExamplesExamples of of instructions instructions in in Scenarios Scenarios 1 1 & & 2: 2: (a (a) )Guidelines Guidelines of of Scenario Scenario 1; 1; ( (bb)) guidelines guidelines of of ScenarioScenario 2; 2; the the Chinese Chinese words words on on the the floor floor mean mean boarding boarding area area and and alighting alighting area.

RegardingRegarding the the existing existing measures measures for for optimizing optimizing the the alighting alighting and and boarding boarding process process in in a a metro metro station,station, no no matter matter the the way way they they increase increase spatial spatial reso resourcesurces or orseparate separate conflicts conflicts between between passengers passengers in spacein space or time, or time, they they all allaim aim to toreduce reduce conflict conflict be betweentween passengers, passengers, making making their their movements movements smoother smoother andand more more efficient. efficient. This This is is also also a a key key issue issue that that transit transit operators operators need need to to consider consider when when organizing organizing passengerspassengers or or setting setting up up the the facilities facilities of of a a metro metro station. station. There There are are still still some some weaknesses weaknesses in in this this study. study. ForFor example, example, the the different different queue queue lengths lengths at at differ differentent doors doors of of a a metro metro vehicle vehicle were were not not taken taken into into account,account, and and the the priority priority of of passengers passengers in in differen differentt directions directions was was not not discussed discussed in in detail. detail. The The study study ofof the the interaction interaction between between passenger passenger organizing organizing an andd resources resources was was poor, poor, and and so so was was the the analysis analysis of of sustainablesustainable development. development. In In subsequent subsequent research, research, it it is is necessary necessary to to overcome overcome the the above above shortcomings shortcomings andand conduct conduct some some research research on on the the relationship relationship between between transportation transportation and and the the environment environment [53,54], [53,54], thethe interaction interaction betweenbetween resourcesresources utilization utilization and and its its pattern pattern of utilizationof utilization [55], [55], and theand impact the impact of public of publictransit transit on urban on urban life [56 life,57 ].[56,57].

Author Contributions: Q.G. and J.Y. recorded and processed the video data of the metro station. Y.J., L.G. and J.Y. Author Contributions: Q.G. and J.Y. recorded and processed the video data of the metro station. Y.J., L.G. and improved the existing social force model and wrote this paper. J.Y. improved the existing social force model and wrote this paper. Funding: This work was financially supported by the National Key R&D Program of China (2018YFB1600900) Funding:and National This Naturalwork was Science financially Foundation supported of China by the (51561135003). National Key R&D Program of China (2018YFB1600900) and National Natural Science Foundation of China (51561135003). Acknowledgments: We are grateful to all the members of the SRTP of SEU and the metro company in Nanjing, China. Acknowledgments:Conflicts of Interest: WeThe are authors grateful declare to all the no conflictmembers of interest.of the SRTP The of founding SEU and sponsors the metro had company no role inin the Nanjing, design China.of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the References decision to publish the results. 1. Rana, M.; Parves, M. Urbanization and sustainability: Challenges and strategies for sustainable urban Referencesdevelopment in bangladesh. Environ. Dev. Sustain. 2011, 13, 237–256. [CrossRef] 1.2. Rana,Du, C.; M.; Xiao, Parves, M.; Zhou, M. Urbanization X. Research onand urban sustainability public transport: Challenges status andand countermeasure strategies for sustainable of yinzhou districturban developmentin ningbo city. in bangladesh. In Proceedings Environ. of the Dev. 2015 Sustain. Ninth 2011 International, 13, 237–256. Conference on Management Science and 2. Du,Engineering C.; Xiao, Management,M.; Zhou, X. Karlsruhe,Research on Germany, urban public 21–23 Julytransport 2015; Volumestatus and 362, countermeasure pp. 471–481. of yinzhou 3. districtYang, X.;in Chen,ningbo A.; city. Ning, In Proceedings B.; Tang, T. Bi-objectiveof the 2015 Nin programmingth International approach Conference for solving on Management the metro timetable Science andoptimization Engineering problem Management, with dwell Karlsruhe, time uncertainty. Germany, Transp.21–23 July Res. 2015; Part Volume E Logist. 362, Transp. pp. 471–481. Rev. 2017 , 97, 22–37. 3. Yang,[CrossRef X.; Chen,] A.; Ning, B.; Tang, T. Bi-objective programming approach for solving the metro timetable 4. optimizationPretty, R.L. The problem planning with of dwell bus transport time uncertainty. for developling Transp. urbanRes. Part form. E Logist.Aust. Transp. Road Res. Rev.1988 2017, 18, ,97 82–88., 22–37. 4.5. Pretty,Harris, R.L. N.G. The Train planning boarding of bus and tr alightingansport for rates developling at high passenger urban form. loads. Aust.J. Adv.Road Transp.Res. 19882010, 18,, 4082–88., 249–263. 5. Harris,[CrossRef N.G.] Train boarding and alighting rates at high passenger loads. J. Adv. Transp. 2010, 40, 249–263. 6.6. Jia,Jia, N.; N.; Wang,Wang, Y.Y.A A Model Model of High-Densityof High-Density Passenger Passenger Boarding Boarding and Alighting and Alighting in Urban in RailUrban Transit Rail StationTransit; Springer:Station; Springer:Singapore, Singapor 2018; pp.e, 2018; 779–789. pp. 779–789. 7. Levinson, H.S. Analyzing Transit Travel Time Performance; Transportation Research Record 1983, 915, 1–6. 7. Levinson, H.S. Analyzing Transit Travel Time Performance. Transp. Res. Rec. 1983, 915, 1–6. 8. Guenthner, R.P.; Sinha, K.C. Modeling Bus Delays Due to Passenger Boardings and Alightings; Transportation 8. Guenthner, R.P.; Sinha, K.C. Modeling Bus Delays Due to Passenger Boardings and Alightings. Transp. Res. Rec. Research Record: 1983. 1983, 915, 7–13. 9. Heinz, W. Passenger Service Times on Trains Theory, Measurements, and Models. Doctor Thesis, Royal Institute of Technology, Stockholm, Sweden, 2003. Sustainability 2019, 11, 3682 19 of 20

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