DSpace at VNU: An agent-based model for simulation of traffic network status: Applied to Hanoi city

Simulation Applications An agent-based model for simulation of traffic network status: Applied to Hanoi city Simulation: Transactions of the Society for Modeling and Simulation International 1–14 Ó The Author(s) 2016 DOI: 10.1177/0037549716668259 sim.sagepub.com Manh Hung Nguyen1,3 and Tuong Vinh Ho2,3 Abstract In recent times there have been many agent-based simulation models proposed for transportation network simulation Intuitively, these models are applicable for the transportation simulation of modern cities in developed countries where the transportation network is very well organized: Roads are separated into lanes; most transportation occurs using vehicles; and almost all drivers respect the circulation rule However, these models are not suitable for developing countries where the transportation network is unorganized The reasons for this are as follows: (1) there is no lane on the road; (2) most vehicles are motorbikes; and (3) not many drivers respect the circulation rule This paper introduces an agent-based model for transportation network simulation in which each form of transport is modeled as an agent exhibiting full features of unorganized circulation behavior The model is designed for a large scale and instead of displaying the circulation for all individual modes of transport, the model displays only the status of the traffic network The simulation of circulation is therefore considered as a background process The simulation is launched and the results are obtained before being displayed This model is applied to the traffic network of Hanoi to analyze the hot or bottle neck points on the transportation network during rush hours in the city Keywords Traffic network modeling, transportation network simulation, simulation model, multiagent system Introduction Transportation network simulation is an active research field that has attracted many researchers in recent times Consequently, many models and tools have been proposed to date Most of them are agent-based models: Each form of transport could be modeled as an intelligent agent which is able to observe other forms of transport and obstacles to avoid and/or change its own speed and direction in order to reach its destination as fast as possible The transportation network therefore could be modeled as a multiagent system where each agent has some personal attributes such as: physical size, goal (its destination to go), destination route plan, etc Circulation behaviors also include: go to the destination, avoid obstacles, find the shortest or the fastest path, share traffic information to other agents, etc There are many agent-based models proposed in the literature.1–21 Technically, most of these models are intentionally designed for an ideal circulation situation for developed countries: roads are partitioned into lanes; the majority of modes of transport are cars, buses, trains, etc., and the drivers mostly respect the circulation rule (see our summary in Table 1) However, these basic assumptions make these models unsuitable for developing countries where the transportation network is unorganized because (see the Figure of the traffic situation and the circulation culture of the Vietnamese):    most of the streets have no lanes and no one respects the rule that drivers have to follow only one lane on a street; the majority of vehicles are motorbikes; drivers not always respect the circulation rule: they will move their vehicle anywhere as long as there is enough space for them Posts and Telecommunications Institute of Technology, Hanoi, Vietnam Vietnam National University in Hanoi, Hanoi, Vietnam UMI UMMISCO 209 (IRD/UPMC), Paris, France Corresponding author: Manh Hung Nguyen, Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Km10, Nguyen Trai, Ha Dong, Ha Noi, Vietnam Email: mhnguyen@ptit.edu.vn Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Simulation: Transactions of the Society for Modeling and Simulation International Table Summary of recent proposed models regarding the current Vietnamese circulation situation Models AgentPolis1 MATSim2 SUMO3,4 VSimRTI5 Al-Dmour6 Araujo et al.7 Cajias et al.8 Certicky et al.9 Frick10 Grether and Nagel11 Handford and Rogers12 Holmgren et al.13 Mounir et al.14 Ramos et al.16 Taha and Ibrahim17 Xiao et al.18 Xu and Tan19 Zacharewicz et al.20 Zhang et al.21 Vietnamese circulation situation Road Transport Lane Car/bus trunk ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü - ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü Behavior Motor In law Out law ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü Following the theme of our previous work,22,23 this paper presents a simulation model and tool applicable for developing countries where the transportation network is unorganized, and it is then applied to the traffic situation and the circulation culture of Vietnam The model is also based on multiagent systems, in which each mode of transport can be modeled as an intelligent agent which can observe other vehicles and obstacles to avoid and/or change its own speed as well as direction to reach its destination as fast as possible However, in order to increase the ability to consider a large number of agents in the system, instead of displaying all the vehicles and their trajectories, the proposed model shows only the status of the streets which represent the actual level of congestion for each street and for the overall network All of the remaining calculations for the vehicle agents are carried out in the lower level of the model This paper is organized as follows: Section presents our model Section presents the modeling of the agents in the model Section presents the application of the model to simulate the traffic network of Hanoi Finally, Section gives a conclusion and draws some perspectives for future work Figure Differences in the circulation situation in developed countries and Vietnam (a) Circulation in Europe (source: www.pressoffice.pl) (b) Circulation in Vietnam (source: www.sgtt.vn) Agent-based model for simulation of traffic network status The model is divided into two main levels (Figure 2): First, the low level which contains mostly processing and calculations This simulates the real transportation network Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Nguyen and Ho surface of the simulation zone (entry city); N is the total population of the simulation city Step 3: Generate agent plans This step creates plans for the vehicle agents A plan contains the following information:      start time: the start time to begin movement; departure: the start place (longitude (x), latitude (y)) for the movement path; destination: the finish place for the movement path; max speed: the maximum speed of the vehicle; type of vehicle: the type of vehicle on the road A vehicle could have several plans with different destinations and start times Step 4: Simulation A vehicle is simulated by an agent which circulates based on its daily plans The actual position of an agent on the road is determined by two factors: Figure The two levels, including their main steps in the proposed model throughout the day and then calculates the level of congestion in the streets of the network This data could be saved into storage and then displayed at a later time Second, the high level, which plays only the role of displaying the simulation data that is already calculated at the low level and stored in storage This section presents the main steps in both of the levels The modeling of vehicles will be presented in the next section Step 1: Load GIS files This step reads the input GIS (Geographic Information System) data to create the road network The GIS data represents the realistic data from the real road network In the input GIS data, each road is characterized by its:       ID: road identification; name: road name; direction: one way or dual ways; permitted vehicles: types of vehicle which could circulate on it; capacity: the width or through put of road; lanes: number of lanes Step 2: Initiate agent position This initiates the start place of the vehicles The location of a vehicle is determined by a point within a zone Thus the number of agents in a specific zone z is determined as follows: s(z) ÃN ð1Þ n(z) = a à I(z) à S where a is the simulation ratio regarding the real size of the population; I(z) is the density of the population in the zone z; s(z) is the surface of the zone z; S is the overall   its movement path: this is dynamically determined by an optimal algorithm (modeled in Section 3.1); its speed: the actual speed of the agent, which could be changed by the following rules: – accelerate: when there is neither an obstacle nor a red light in front of it and its speed is not maximal yet; – no change: when there are some obstacles in front of it such that it can not pass, or its speed is already maximal; – slow down: when there are many obstacles in front of it with decreasing speed Step 5: Observation and sampling This step observes the actual throughput of streets in the network at some moments in time (called sampling time), and then saves the data into storage In our case study of the Hanoi traffic network, we categorise four kinds of road status: blocked, very slow, slow, normal speed The threshold speed for each status can be estimated by using a Regular Increasing Monotone (RIM) linguistic quantifier Q (Zadeh24) In our case study we use Q(x) = x3 In Vietnam, the circulation policy and rule limit the speed in urban zones to 40 km/h for motorbikes, and 50 km/h for cars, taxis, trunks and buses The actual speed of a crowd is estimated as the speed of the slowest member in the crowd So we use the limiting speed of a motorbike as the reference speed (max speed in the fourth row of the Table 2) for our estimations Consequently, the threshold speed for each status is estimated as presented in Table 2:  blocked road: the speed is less than km/h; Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Simulation: Transactions of the Society for Modeling and Simulation International Table Threshold speed for road status Status Blocked Very slow Slow Normal Step (i) Q (i/4) Max speed (km/h) Threshold speed (km/h) 1/64 0.625 [0,1] 8/64 (1,5] 27/64 16.8 (5,17] 40 (17,40]      very slow road: the speed is between km/h and km/h; slow road: the speed is between km/h and 17 km/h; normal road: the speed is higher than 17 km/h Step 6: Display road/network status This step reads the traffic status data for all of the streets from the storage carried out in step and then displays the data on the network Each kind of traffic status is represented by a color:   red: the road (intersection) is currently blocked; orange: the road is almost full, the movement of vehicles is very slow; yellow: the road contains many vehicles, the movement of vehicles is slow; gray or green: the road has a normal traffic, the movement is normal df = 10 This section presents the modeling of two kinds of agents in the system: the vehicle agent and the traffic light agent Each agent is classified using two parts: attributes, and behaviors 3.1 Vehicle agent This agent represents a transport such as a trunk, a bus, a car (including taxi) or a motorbike All of these vehicles are controlled by a driver Therefore, we not need to separately model drivers and can instead consider the driver and his vehicle as a unique entity, called a vehicle agent 3.1.1 Attributes A vehicle agent has the following attributes Name: name or type of vehicle Length (denoted as l): the physical length of the vehicle Width (denoted as d): the physical width of the vehicle lÃy +u y max ð2Þ where u is the minimal distance allowed between two consecutive vehicles when at rest Safety beside distance (denoted as db ): the minimal distance to the nearest left/right side of the vehicle such that the movement is still safe This distance is calculated as follows: db = 11 Agent modeling Max speed (denoted as y max ): the maximal speed which is allowed by law Current speed (denoted as v): the actual speed of the vehicle at a moment in time Max technical speed (denoted as ytech ): the maximal technical speed that a vehicle engine could reach Safety front distance (denoted as df ): the minimal distance to the nearest vehicle in front (or behind) of it such that the movement is still safe This distance is calculated as follows: dÃy +u y max ð3Þ Acceleration factor (denoted as a): the increase of speed in a unit of time Deceleration factor (denoted as b): the decrease of speed in a unit of time A set of circulation plans which represents the route plan of the vehicle in a daytime The formulas for df and db say that the higher the actual speed of the vehicle, the greater the safety distance must be between vehicles 3.1.2 Behavior In order to simulate the unorganized circulation activities such as that in the situation of Vietnam, we need to distinguish two kinds of vehicle agent: obedient and disobedient The obedient vehicle agent is the one who respects completely the circulation laws This agent has the following activities Find a path: finds a path to go to its destination This activity occurs when: either the agent starts a new plan; or it has to change the path when its current path is blocked somewhere Observation: captures the changes in its environment such as observing traffic lights and detecting other obstacles to avoid during circulation Stop: stops at an intersection when the traffic light is red or at the destination of its plan Accelerate: increases its speed This activity occurs when: (1) there is enough space in front of it to accelerate; and (2) its current speed could be Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Nguyen and Ho increased in a low speed regime The new speed will accelerate to: y t + = minfy t + a, y max g ð4Þ where yt , y t + are the speed of the vehicle agent at the simulation steps t, t + 1, respectively Decelerate: decreases its speed This occurs when: (1) there is some obstacles in front of it; or (2) it wants to stop somewhere The new speed will decelerate to: y t + = maxfy t À b, 0g ð5Þ The disobedient vehicle agent is the one who usually breaks the circulation law This agent has some differences in the following activities: Accelerate: it increases its speed as long as there is enough space in front of it and its speed is still lower than the y tech The new speed will accelerate to: y t + = minfy t + a, y tech g ð6Þ where y t , yt + are the speeds of the vehicle at the simulation steps t, t + 1, respectively Decelerate: the same principle as with the obedient agent The transition among activities of a vehicle agent is presented in Figure 3: When the time is the same as the start time of a plan, the vehicle agent starts to move Firstly, it finds a path to go its destination During movement, it captures the occurrence or the change of status of three kinds of object: the traffic light, the destination, and the obstacle At an intersection, it observes the change of traffic light color: if the light is red it will stop; otherwise, it will continue to move At any point on the path, it will accelerate if there is no obstacle in the street and its actual speed y is still lower than the allowed speed y max In contrast, it will decelerate if there are some obstacles or its speed y is already higher than the allowed speed ymax (for the case of an obedient vehicle agent) Otherwise, it continues to travel with the same speed It could re-find the path if it was blocked somewhere If it is already at the destination, it stops 3.1.3 Dynamic optimal path finding There have been many models proposed recently to optimize the routing of the whole traffic network by searching for the best path for each individual such as the models by Deng et al.,25 Hua and Pei,26 Nannicini et al.,27 Efentakis et al.,28 This paper introduces an algorithm to dynamically optimize the routing at the system level of a transportation network In this algorithm, each individual will be recommended to follow a path which depends on the current traffic capacity and the user preferences such that the overall throughput of the traffic network is maximized as well as the user preferences being satisfied Construction of the graph for a traffic network The graph for a given traffic network will be created as follows: Each intersection forms a node of the graph Each road forms an arc with the same direction If a long road has many segment points where the drivers could change direction, then each segment Figure Behavior of the transport agent Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Simulation: Transactions of the Society for Modeling and Simulation International of road (between two consecutive segment points) will form an arc with the same direction Let yavg be the average speed of circulation (y avg = average(yk ), for 8k on the same road from node i to node j), lengthij be the length of the road from node i to node j, then the necessary time tij to pass through the road from node i to node j will be estimated as follows: tij = lengthij y avg ð7Þ In fact, the weight of an arc could be a value which is propositional with one of two options: (i) the length of the corresponding road; (ii) the necessary time to pass the road corresponding to the arc We use the second approach because it takes into account the dynamic nature of the traffic network whereas the first one is always static Therefore, we call the best path from an original point to an end point the fastest path instead of shortest path Static fastest path The objective of this step is to estimate the fastest path for the ideal circulation conditions: we can drive with maximum allowed speed on all roads in the network: y = ymax , so yavg = ymax With regards to Equation (7), when y avg = y max , then tij becomes constant That is why we call this the SFP - Static Fastest Path User preference We aim to take into account the variations in time constraints among drivers by modeling the user preference Thus, the preference on the time constraint of a driver is represented by a number K, K 1: The maximal acceptable time of this driver is K à SFP (K times the static fastest path in the ideal conditions) The bigger K, the less important the user places on time; and vice versa, the smaller K, the more strictly time is constrained for the user In respecting the user preferences, the dynamic fastest path has to be shorter than K à SFP This idea also helps us to prevent too many people from being recommended to follow the same road because their preferences are different regardless of it they have the same origin and end point Dynamic path optimization algorithm Let o G be the original node, e G be the destination node, H  G be the set of visited nodes, U  G be the set of un-visited nodes, d(i, j) be the fastest time from node i to node j and P(i) = j represents the node j that is the node just before node i on the fastest path from the original node o to node j The algorithm is as follows: Initiation Put node o into H, d(i, j) = tij with tij estimated from Equation (7), P(i) =À Repeat Considering all nodes j U where there is at least one node l H which has a direct arc from k to j and d(o, l) + tlj K à SFP:  if d(o, j) d(o, l) + tlj then: P(j) = l and d(o, j) = d(o, l) + tlj ;  put j into G, remove j from U;  if e is already in G or U is empty then stop Constructing of the fastest path From P(e), find P(P(e)) until o is reached Reverse this series and we will obtain the track of the fastest path from o to e 3.2 Traffic light This agent represents a traffic controller at an intersection of the traffic network This agent can not move It only has the ability to change the color of the traffic light to route the traffic 3.2.1 Attributes A traffic light has the following attributes: A set of control directions: These are all directions which are designed at an intersection (e.g., Figure presents four control directions for an intersection) Each control direction has the following:  green duration (denoted dg ): This is an interval of time During this interval, the light is green and the vehicles can go through the intersection  red duration (denoted dr ): This is an interval of time During this interval, the light is red and the vehicles have to stop  minimal green duration (denoted MinT): the green duration can not be lower than this threshold to avoid the case that the light color changes too quickly to follow  maximal green duration (denoted MaxT): the green duration could not be longer than this threshold to avoid the case that the remaining control directions have to wait too long  time counter (denoted counter): this agent changes the color of the light when the value of this variable reaches the green or red duration 3.2.2 Behavior A traffic light has three behaviors: Change to green: this activity occurs when the current light is red and the counter value is equal to dr After changing to green, the counter value is reset to zero Change to red: this activity occurs when the light is green and the counter value is equal to dg After changing to red, the counter value is also reset to zero Optimization of green/red time: this agent dynamically updates the dg and dr to optimize the traffic routing at its intersection The algorithm will be presented in the next section Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Nguyen and Ho transportation network In this algorithm, the next green time of a direction is estimated based on the rate of modes of transport which pass through the intersection during the last green time In the case of an independent light direction, the main steps in the algorithm are the following: Figure Four control directions at an intersection The green light time must not be lower than a minimal threshold MinT , and not higher than a maximal threshold MaxT In all cases, the light is set to green if the destination direction still remains for the mode of transport Let ntÀ1 , nt be the number of modes of transport which passed the green light during the (t À 1)th, tth green light times, respectively Let dt be the green light time at t The green light time at (t+1) will then be:   dt à nt dt + = max ( , MinT ), MaxT ntÀ1 ð8Þ In the case of several light directions, the main steps in the algorithm are as follows: Figure Behavior of the traffic light agent The transition among activities of a traffic light agent is presented in Figure Firstly, it initiates the value of dr , dg , and the color for each of the control directions Then the transition between change to red and change to green is controlled by the value of counter, by comparing it to the value of dr and dg During this transition loop, the function of Optimization of green/red time is also evaluated The green light time must not be lower than a minimal threshold MinT , and not higher than a maximal threshold MaxT In all cases, the light is set to green if the destination direction remains for the mode of transport Let k be the number of control directions at the intersection Let nt (i) be the number of modes of transport which passed the green light, for the control direction i, during the tth green light time Let dt (i) be the green light time at t of the control direction i The green light time at (t + 1) will be:   nt (i) à MinT , MaxT dt+1 (i) = minj = (nt (j)) ð9Þ The red light time at (t + 1) will be: dt0 + (i) = k X dt + (j) ð10Þ j = 1, j6¼i 3.2.3 Dynamic optimal routing for traffic light There are many models proposed in recent times to optimize the routing at an intersection of a transportation network, such as the models by Burguillo-Rial et al.,29 Royani et al.,30 Mehan and Sharma,31 Popescu et al.,32 Gershenson and Rosenblueth.33 This paper introduces an algorithm to dynamically optimize the routing at an intersection of a A case study: Simulation of the traffic network of Hanoi city This section presents a case study in which the proposed model is applied to simulate the traffic network of Hanoi (the capital of Vietnam) Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Simulation: Transactions of the Society for Modeling and Simulation International Table Population distribution by central districts District No of quarters Surface (km2) Population (1000 people) Ba Dinh Cau Giay Dong Da Ha Dong Hai Ba Trung Hoan Kiem Hoang Mai Tay Ho Thanh Xuan 14 21 17 20 18 14 11 9.22 12.04 9.96 47.91 9.60 5.29 41.04 24 9.11 235.7 237.0 379.2 217.7 325.6 156.6 329.0 139.2 252.0 Table Initial value of vehicle agent’s parameters Parameters Motor Car/taxi Bus/trunk Name Length (l) Width (d) Max speed (max ) Max technical speed Min distance allowed Accelerate factor (α) Decelerate factor (β) motor 2.0 m 0.8 m 40 km/h 120 km/h 1m 0.5 m/s2 0.5 m/s2 car 4.5 m 1.8 m 50 km/h 150 km/h 4m 0.5 m/s2 0.5 m/s2 bus 11.5 m 2.5 m 50 km/h 150 km/h 10 m 0.5 m/s2 0.5 m/s2 4.1 Simulation setup This section presents the initiation of the agent population and the agent parameters and the construction of the agent plan 4.1.1 Initiation of agent population The position of agents is initiated based on the realistic population distribution of Hanoi, grouped by districts (Table 3; the statistical numbers were collected from multi sources in 2013) For instance, if we take the simulation rate as 1:100, there will be about 23,000 agents in the nine central districts In which there will be about 2200 agents living in Ha Dong district, and 2500 other agents living in Thanh Xuan district, etc 4.1.2 Initiation of vehicle parameters The initial conditions for the vehicle parameters is presented in Table The values of size, max technical speed, accelerate factor, and decelerate factor for each kind of vehicle are assigned based on the majority of vehicles which circulate in Vietnam The value of max speed, and minimal distance allowed are defined based on the Vietnamese circulation law for urban zones 4.1.3 Construction of agent plans The data about the distribution of buildings in the city such as offices, hospitals, schools, universities, tourist sites, commercial centers, manufactures, etc is loaded from a GIS file (Figure 6) This is the input data used to build the vehicle agent plan: its home position, its jobs will determine an office or a school For instance, consider a woman officer who lives in Thanh Xuan district She uses her car to take her son to school before a.m at Kim Lien quarter Then, she goes to her office in Ba Trieu street before a.m At the end of day, she leaves her office at 4:30 p.m and gets to her son’s school to collect him at p.m after which the mother and son go back home together (Figure 7) Figure Distribution of buildings in Hanoi Figure Representation of an individual’s plans in XML format 4.2 Results This section presents some results of the simulation considering several aspects: runtime, validation of Vietnamese circulation behavior, displaying the traffic network at different levels, analyzing the statistics, and the validation of the dynamic optimum of path finding 4.2.1 Simulation runtime The simulation runtime depends on the configuration of the running machine, as depicted in Table The chosen simulation rate is 1:100 This means that the number of vehicle agents is about 23,000 Consequently, the simulation time on a normal PC, for a Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Nguyen and Ho Table Simulation configuration and runtime Parameters PC Server CPU speed RAM HDD OS Simulation rate No vehicle agents day simulation time day display time 1.80 GHz GB 100 GB Windows 1:100 23,000 ∼ 15 hours ∼ minutes 3.0 Ghz 16 GB 120 GB + TB Windows server 1:100 23,000 ∼ hours ∼ minutes whole day (24 h) of simulation, is about 15 hours Meanwhile, the simulation time on a server with high configuration and performance is about hours Independent of the machine configuration, the display time in both cases is only about minutes, including the delay time after each sample status display The display time is much less than the simulation time because it simply reads the simulation results (which are obtained from a long simulation time and already saved into a file) and then displays them on the interface These results confirm the advantage of our approach by separating into the two levels: simulation and display Despite the length of the simulation time and the complexity of the simulation system, the display time could be short enough to display as a real-time simulation 4.2.2 Validation of Vietnamese circulation behavior In order to validate the Vietnamese circulation behavior in the model, we consider the following scenario:    track the circulation path of an obedient driver at a low traffic moment; track the circulation path of a disobedient driver at a high traffic moment; display the two tracked paths on the passed streets to see their differences The results are presented in Figure There is a big difference between the two tracked paths The path of the obedient driver is much more smooth than that of the disobedient driver The path of the disobedient driver is less structured because the driver adopted the Vietnamese circulation behavior during high traffic moments: he did not respect the circulation law, the signal of the traffic lights, as well as the circulation lane He moved forward as long as there was enough space in front of him to so This path illustrates the circulation behavior of many Vietnamese drivers in the high traffic moments which is modeled in the proposed model 4.2.3 Displaying of traffic at the local level This model also enables us to show the traffic situation on a smaller scale Figure Circulation path for the obedient and disobedient drivers (a) Obedient driver’s path at 6:00 a.m (b) Disobedient driver’s path at 7:20 a.m for the traffic network, such as the traffic in a particular street, at an intersection, or at any hot point of the traffic network For instance, the traffic at an intersection is presented in Figure for several situations: the traffic in low traffic (at a.m.) or a high traffic moment (at 7:20 a.m.) and the traffic when the traffic lights work or not work It is easy to see that, with the same traffic conditions, the circulation when the traffic lights work is better than that when the traffic lights not work The circulation in a low traffic moment is better than that in rush hour The circulation in rush hour without traffic lights (Figure 9(d) simulates completely the real traffic situation depicted in Figure 1(b) 4.2.4 Displaying of traffic status at the overall level The results of traffic network status are presented in Figure 10 At a.m., there is no red or orange street and there are only some streets in yellow because it is not a rush hour yet At 7:20 a.m., it is a rush hour as there are at least five Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 10 Simulation: Transactions of the Society for Modeling and Simulation International Figure Traffic, in local view, at different moments during the day (a) 6:00 a.m., with traffic lights (b) 6:00 a.m., without traffic lights (c) 7:20 a.m., with traffic lights (d) 7:20 a.m., without traffic lights Figure 10 Traffic status, in global view, at different moments during the day (a) 6:00 a.m (b) 7:20 a.m (c) 12:00 p.m (d) 06:00 p.m Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 Nguyen and Ho 11 Figure 11 Detailed view on a point Figure 12 Some analysis at different instances streets in red, many in orange and in yellow Meanwhile, the number of streets in red at p.m is lower than those at 7:20 a.m., but it is still higher than those at 12 p.m This analysis of traffic network status enables us to track and to compare the overall status of the traffic network at many daily moments These also show whether a particular moment is considered a rush hour or not In order to describe more detail on the traffic network, our tool also enable us to see in detail at any point on the network by clicking on it There will be a small window with the detail information appears For instance, as depicted in Figure 11, when we click on the crossroads of st Ton That Tung and st Truong Chinh at 7:20 a.m., a window appears to show more detailed information: the instant throughput of st Truong Chinh is about 95.5% (this street is in red), while that of st Ton That Tung is about 92.3% (this street is in orange) Moreover, other information involving the streets is also displayed: name of the street, type of street (one way or not), etc 4.2.5 Analysis level As data is tracked and saved as xls files, it is easy to analyze for any strategy or direction For instance, we could compare the traffic status in a day between any two streets as depicted in Figure 12(a) for the st Tran Duy Hung and the st Pham Ngoc Thach We see that there in no difference between their throughput in low traffic hours Inversely, in rush hours, the level of congestion on the st Pham Ngoc Thach is generally higher than that on the st Tran Duy Hung, especially during 7–8 a.m and 5–7 p.m Another analysis we could take is to on overall network, in particular, the statistics on the levels of street congestion (Figure 12(b)) In this analysis, we count the number of streets with the same level of congestion and compare them together The results indicate that most rush hours are 7–8 a.m and 5–6 p.m During other times, there are not many streets in congestion These results are in good agreement with the real situation for these streets Based on the long observation of daily reports on media express (e.g, The traffic Voice of Vietnam - VOV radio channel) about these streets, there is always congestion or blocked points on these streets during rush hours, such as in 7–8 a.m and 5–7 p.m 4.2.6 Validation of the dynamic path finding We consider the effect of the path finding algorithm for two different traffic conditions: low and high traffic conditions The results are presented in Figure 13 In the case of low traffic conditions (all roads are in green status - normal speed, Figure 13(a)), there is no difference between the located fastest path and the physical shortest path (Figure 13(b)) This is intuitive because in low traffic conditions, the time to pass a long a street is linear with respect to the length of the street This is why the fastest path is the same as the shortest path In the case of high traffic conditions (many roads are in red, orange, yellow - Figure 13(c)), there are some significant differences between the located fastest path and the physical shortest path (Figure 13(d) versus Figure 13(b)) Because the fact that when a street is blocked, the time to pass a street may be longer the time to pass an other street which could be longer in physical length but is not blocked and the vehicles would be able to pass this street with a higher speed This is the main idea of the dynamic optimization Conclusion This paper proposed an agent-based model in which each transport agent was modeled with full features of unorganized circulation behavior The model is designed for a large scale: instead of visualizing the circulation of all individual modes of transport, we instead visualized only the status of the traffic network, and the simulation of the Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 12 Simulation: Transactions of the Society for Modeling and Simulation International circulation is considered as a background process This approach enabled us to display the traffic network status at any moment in time This model is also useful to simulate the traffic network status for any city as long as we have data about the real traffic network, the population distribution, and the jobs/age distribution of the city This model was applied to the traffic network of Hanoi where most 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Self-organizing traffic lights at multiple-street intersections Complex 2012; 17(4): 23–39 Author biographies Manh Hung Nguyen has been a lecturer at The Posts and Telecommunication Institute of Technology (PTIT), Hanoi, Vietnam, since 2011 He received his PhD degree in Computer Science at the University of Toulouse III Paul Sabatier, France, in 2010 His domains of interest are: artificial intelligence, multiagent systems, modeling and simulation of complex systems, and distributed intelligent computing Tuong Vinh Ho is a researcher–lecturer at Institute Francophone International (IFI) since 2000 Currently, he Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 14 Simulation: Transactions of the Society for Modeling and Simulation International is Vice-Director of IFI, in charge of research activities, and Head of MSI (Computational Modeling and Simulation of Complex Systems) research team at IFI He holds a PhD degree in Computer Engineering from the Ecole Polytechnique de Montreal (1999) His research interests include software engineering, computational modeling, and simulation of complex systems Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 ... status of the traffic network, and the simulation of the Downloaded from sim.sagepub.com at CORNELL UNIV on October 12, 2016 12 Simulation: Transactions of the Society for Modeling and Simulation. .. than those at 7:20 a.m., but it is still higher than those at 12 p.m This analysis of traffic network status enables us to track and to compare the overall status of the traffic network at many... 15 Nakajima Y, Yamane S and Hattori H Multi -model based simulation platform for urban traffic simulation In: Desai N, Liu A and Winikoff M (eds) Principles and practice of multi-agent systems