Nowadays, both travel guidance systems and traffic signal control systems are quite common for urban traffic management. In order to achieve collaborative effect, different models had been proposed in the last two decades. In recent years, with the development of variable message sign (VMS) technology, more and more VMS panels are installed on major arterials to provide highly visible and concise graphs or text messages to drivers, especially in developing countries. To discover drivers’ responses to VMS, we establish a drivers’ en route diversion model according to a statedpreference survey. Basically, we proposed a cooperative mechanism and systematic framework of VMS travel guidance and major arterials signal operations. And then a twostage nested optimization problem is formulated. To solve this optimization problem, a simulationbased optimization method is adopted to optimize the cooperative strategies with TRANSIMS. The proposed method is applied to the real network of Tianjin City comprising of 30 nodes and 46 links. Simulations show that this new method could well improve the network condition by 26.3%. And analysis reveals that GA with nested dynamic programming is an effective technique to solve the optimization problem.
In recent years, China has entered the stage of quickened urbanization process. However, traffic problems such as traffic congestions and the deterioration of air quality are getting increasingly serious. Since the obvious unbalance between the rapidly growing traffic demand and the limitation of traffic resources, traffic management is becoming an increasingly serious concern. During the last two decades, how to alleviate traffic congestion by better urban traffic management strategies has captivated consistent attention from both traffic operators and engineers.
To reach this goal, various strategies have been developed for urban traffic management [
Another common urban traffic management method is traffic signal operational strategy, which provides a realtime traffic flow control approach [
In previous attempts, the cooperative mechanism related to traffic signal control and travel guidance is one of hotspots for researchers. In the early stage, most research integrates traffic signal control with user equilibrium [
To solve the aforementioned problems, some researches have been conducted on cooperation of en route travel guidance and traffic signal control. Among en route travel guidance approaches, advanced onboard navigation systems or traffic information broadcasting can provide realtime information and guidance advice for users. However, there are still some problems needed to be solved before using in developing countries: users of navigation systems only cover a tiny proportion of total travelers in developing countries; and it is isolated from signal control system. In general, these systems designed for travellers have to be separated from signal control systems for security reasons. Different with above approaches, the VMS strategically located on the roadside can directly affect drivers’ en route choice. Moreover, VMS technique allows the information communication with road detectors and traffic signal controllers.
In recent years, several studies on VMS travel guidance and traffic signal control have been carried out for urban traffic management in developing countries [
In this paper, we first establish a logit model of drivers’ responses to graph and text information on VMS panels based on a statedpreference survey. And then a cooperative strategy and systematical framework of VMS and traffic signal control are proposed for major arterials. Within this framework, the optimization of the cooperative strategy can be represented as a twostage nested optimization problem. We name this optimization problem the VMS guidance and signal coordination (VGSC) problem. The desired road status display, diversion advice, and arterial signal parameters (i.e., cycle times and green times) are updated in the firststage optimization problem by using genetic algorithms. Subsequently, arterial signal offsets are optimized by dynamic programing method in the secondstage. Considering the complexity of interactions between vehicles and signalized or unsignalized intersections on a large network, we further develop a simulationbased optimization (SBO) software package with TRANSIMS to solve the twostage nested optimization problem.
To explain the mechanism and application of the cooperative strategy, the rest of this paper is organized as follows. Section
Many studies have investigated the impact of VMS on drivers’ behavior [
In developing countries, VMS are widely used in urban road network. These VMS panels allow releasing information on “congestion scale,” that is, road network with different colors. The “congestion scale” is a classification of average speed calculated by travel time (i.e., red means congested road segment; yellow means heavy traffic volume segment; and green means lowtraffic segment). In detail, for major arterials, red represents the realtime speed lower than 20 km/h; yellow represents 20 km/h–40 km/h; and green represents higher speed than 40 km/h. (see Figure
An illustration of VMS panel.
To solve existing problems of drivers’ responses to graphical information, we carried out a statedpreference (SP) survey, which is coded into pad computers. Considering road segments with different colors and detour distances that drivers might face in the urban area, various variables including distances of pretrip path (AB) and detour route (ACDB), diversion advice, and colors were changed randomly.
Multiple factors visàvis VMS have been considered in the SP survey and the collected information can be classified in the following categories:
personal information: age, gender, and salary; and the personal attributes which define drivers’ characteristics have been identified in Table
preference information:
a drivers’ acceptability of VMS;
a drivers’ response to information on VMS;
dynamic information on VMS panels:
distances: original route and alternative routes;
colors: yellow and red segment ratios;
diversion advice.
Summary of selected personal attributes.
Personal attributes  Percentage (%)  

Gender  Male  44.94 
Female  55.06  
Age  18–29  39.33 
30–49  58.43  
50–69  2.25  
Personal salary  <50,000  15.73 
50,001–100,000  21.35  
100,001–150,000  20.22  
150,001–200,000  11.24  
200,001–250,000  16.85  
250,001–300,000  1.12  
300,001–350,000  4.49  
350,001–400,000  1.12  
>400,000  7.87 
On the basis of the SP survey, drivers’ response to VMS is typically modeled by a binary logit model. There are two alternatives, which are denoted by 0 or 1 (i.e., 0 means keeping original route and 1 means choosing to divert). In order to recover all related factors, we take multiple variables into consideration, as follows:
age: the drivers’ ages;
gender: a dummy variable, which is equal to zero if the driver is male and one otherwise;
route length: the length of routes
advice: a dummy variable which is equal to zero if the VMS shows the advice and one otherwise;
where
By analysis of collected behavioral data, we managed to find a set of significant factors. A binary logit model is presented in Table
Binary logit model for drivers’ responses to VMS.
Variables  Coefficient  Estimates  Standard.Error  Significance 

Age 

0.044  0.017  0.010 


0.965  0.096  0.001 


−0.439  0.076  0.001 


3.510  0.606  0.000 


−6.240  1.371  0.000 
Advice 

0.431  0.211  0.041 
Constant 

−3.032  1.314  0.021 
In addition, the utility function is formulated as
The probability that individual
In this section, we propose a cooperative mechanism of VMS and traffic signal control system in urban areas. In order to improve effectiveness of cooperation, a twostage nested optimization problem is formulated.
Based on the proposed logit model in Section
This important improvement of the active VMS strategy is achieved by two preconditions: first, we allow the cooperation of VMS and TSC systems instead of isolation ones; second and more importantly, we can find a desirable solution by joint optimization in a short term and the solution has to be consistent with drivers’ driving feelings for a sustainable effectiveness in a longterm use. This approach strikes a very nice balance between accessibility and effectiveness.
By considering signal cycle length and the location of VMS panels, the control period
When it comes to the aforesaid first precondition, both the VMS and TSC systems are installed and controlled by transportation management agencies. The implementation of the joint optimization for the two systems in the control center is easy to complete.
On the other hand, when it comes to the second precondition, some ifthen rules should be satisfied for the optimization process. We assume that the control result is
Equation (
As the mechanism mentioned above, how to find a desirable solution of VMS travel guidance and traffic signal control can be represented as an optimization problem. We name this optimization problem the VMS guidance and signal coordination (VGSC) problem. To solve this problem, traffic signal parameters (i.e., cycle times, green times, and offsets) of arterials shown on the VMS panels should be optimized simultaneously with these arterials’ colors and diversion advice. In other words, all variables in (
Among those variables, the offsets need a special attention to their optimization. For major arterials having a number of signalized intersections, optimal offsets under fixed cycle times and green splits need to be selected from a (
In the first stage, VMS parameters and arterial signal parameters are optimized in order to minimize the total travel time of travellers on a road network. The firststage optimization problem can be expressed in
OD constraints,
VMS constraints in (
Signal control constraints,
In the secondstage, the optimization of
In this section, we provide a simulationbasedoptimization (SBO) method for the twostage nested optimization problem. A SBO systematic framework is put forward in Section
Return
To better illustrate the cooperative mechanism, we propose a systematic framework in Figure
A SBO framework for the VGSC Problem.
With multiple decision variables in the twostage optimization problem, the interdependence between VMS and traffic signal control is hard to evaluate by a traditional optimizer. In addition, a microscopic traffic simulator can give a relatively accurate estimation of flow distribution, average speeds, and interactions of vehicles.
Therefore, we adopt the SBO method to solve the aforementioned optimization problem. In Figure
The SBO framework aims to obtain the feasible optimal strategies that minimize total travel time of all travellers. And the simulationbased algorithms are explained in detail as follows.
As previously mentioned, the optimization of VMS and TSC is difficult to find optimal solution mathematically. As suggested in [
As indicated in (
After simulation of each individual, the fitness function is applied to each solution indicating how close it meets the overall specification. Based on cellar automata approach, microsimulator can produce specific information for every traveller. Therefore, the temporal summary of total travel time and average speed over a segment of link can be aggregated in given time increments. It allows us to compute the objective function and constraints.
In a genetic algorithm, a population of candidate solutions is evolved toward better solutions. Each candidate solution has a set of variables (i.e., VMS parameters:
Because of the nature of genetic algorithms, wrapping or truncating individuals in a generation has great influence on optimization performance. In order to deal with the constraints mentioned above without noises, we take advantage of a penalty scheme to the evaluation function. It provides a penalty to the fitness, which is proportional to the constraint violation.
The iterative process is repeated until a termination condition has been reached. The terminating conditions are as follows.
Max generation: reach the max generation.
Convergence criteria: reach a plateau that successive iterations no longer produce better results.
The secondstage optimization problem was the arterial signal offsets optimization under the condition of fixed green time and cycle time. A recent research by Gartner and Rahul developed a dynamic programming (DP) model which is suitable for signal offsets optimization [
The arterial signalized intersection is denoted as
Offsets and travel time functions on a fiveintersection arterial.
By setting offset interval
Every connection in Figure
The average travel time on link
By comparing this average travel time, we could obtain the offset
By repeating the above 3 steps for each link, the optimal offset sequence
A sum of average travel times for the set of links in the arterial is a significant parameter for evaluating the arterial signal control effectiveness. Corresponding to the optimal offset sequence
An illustration of dynamic programming of offsets.
The proposed methodology is applied in the actual road network of Tianjin Binhai HiTech Industrial Development Park (T.H.I.P, China). This site is located 3 km west to Tianjin urban area, with a twosquarekilometer core area. As a connection to the Tianjin municipal area, road network in this core area is almost filled with heavy traffic flow during rush hours of working days.
In this area, more than 80 percent traffic is due to daily commuters, who work in T.H.I.P. Moreover, traffic management rules and traffic signals are properly maintained, while daily traffic congestions still trouble travelers. Therefore, the road network of T.H.I.P is selected for the proposed study (as shown in Figure
Map of T.H.I.P with land use.
Road network topology of T.H.I.P.
Subsequently, the study site network was coded into TRANSIMS manually by using GIS networks. The network creation required static information including zone, node, link, pocket lane, and vehicle composition. Among them, the vehicle composition contains vehicle type, size, capacity, maximum speed, and acceleration. The traffic signal timing plan for arterial signal control consists of cycle time, green time, yellow time, red time, offset, and phase sequence. And the timing plan can be revised by signal control module dynamically.
If the initial simulation result indicates that default parameters of microsimulator module in TRANSIMS are not acceptable, the parameter calibrations are necessary. In this paper, calibrations for the AM and PM peak time periods were conducted. And the urban arterial network mentioned above was calibrated against field measured traffic count data and travel times by video cameras at signalized intersections. Based on the basic steps of parameter calibration in TRANSIMS proposed by Park and Kwak [
The first step is the selection of calibration parameters including lanechanging and carfollowing parameters. In brief, we take the following 5 key calibration parameters as examples.
Subsequently, ranges of calibration parameters are determined and multiple sets of calibration parameters are generated. In detail, slowdown probability ranges from 0 to 50%; slowdown percentage ranges from 0 to 50%; maximum waiting time ranges from 60 to 200 minutes; and maximum swapping speed ranges from 0 to 30 m/s. And then 1000 simulation runs are performed for each parameter set.
The distribution of simulation results is compared to travel times and volumes generated by field video cameras in order to determine whether current parameter ranges are feasible. As a result, the parameters are calibrated and the results are that the slowdown probability is 25.3%, slowdown percentage is 34.5%, maximum waiting time is 83 minutes, and maximum swapping speed is 11.5 m/s.
In the end, it is necessary to conduct 1000 simulation runs to consider the variability of the parameter set.
TRANSIMS is a travel demand modeling software package that was initially developed by the Los Alamos National Laboratory (LANL) [
The opensource software package allows the user to develop new function and customize features of the simulation model. In order to optimize the joint strategy, we develop the driver diversion module, traffic signal control module, and optimization module in Python 2.7. Through integrating with postprocessor of TRANSIMS written in C++, the developed three modules can perform various functions. A flowchart in Figure
Flowchart of simulation program in TRANSIMS.
At first, the driver diversion module extracts driver spacetime information from snapshots files to find drivers who can see the VMS panels. Subsequently, the driver diversion model determines if drivers will make a detour under the influence of VMS information, personal features, and other factors identified previously.
Subsequently, the traffic signal control module can revise signal timing table under the direction of dynamic programming. And total average travel time on arterial links provides a feedback to the signal controllers to evaluate its effectiveness.
The optimization module is developed to evaluate the joint optimization by using genetic algorithm. And the statistical analysis of the simulation results was done to validate the feasibility of solutions and compute the objective value of solutions. The algorithm was implemented in Python using the Pyevolve module [
In this network, all 30 nodes are signalized nodes. Among them, 15 nodes on the two arterials (i.e., path 1 and path 2) can be controlled under the direction of optimization module, and other intersections used the fixed timing table. By using the arterial signal control method, we assume that the cycle time and green time for intersections on an arterial are the same. To make faster computation, we further restrict the range of variables’ values, as follows.
Cycle times: minimum cycle time
Phase sequence: phase sequence of a given node is kept the same.
Green time: minimum green time
Offsets: minimum green time
According to the size of study network, we set the control interval as 5 minutes. And the 12 optimum solutions can be obtained during 8:009:00 in the morning peak time. During each interval, 7 variables are optimized in the firststage GA optimization, including the desired yellow ratios on the two arterials, the cycle times and green times for each arterial, and diversion advice. Moreover the offsets of 10 signalized intersections on the two arterials are optimized by the secondstage dynamic programing.
By the multiruns of the three control methods under traffic demand during the morning peak time, we present the mean values of 20 groups. The comparison of improvement of traffic condition by different strategies is shown in Table
Comparison of minimum values of objective function.
Control method  Index  

Objective function values  Improvement  
Pretimed signal control without VMS  84756.77  / 
Pretimed signal control with VMS strategy  76118.13  10.2% 
The cooperative strategy of VMS and arterial signal control  62391.83  26.3% 
The convergence process of each control interval is further explained in Figure
Convergence process of the genetic algorithm.
The evolution process
The standard deviation of population in generations
Total travel time of population along generations
In this paper, we study variable message sign (VMS) widely deployed on major arterials in developing countries. In order to appropriately describe drivers’ response to VMS, we propose an en route diversion model based on a statedpreference behavioral survey. The vital significance of this new model lies in well considering drivers’ responses to graphical road with colors.
On this basis, we propose a cooperative mechanism of VMS travel guidance and arterial signal control for urban traffic management. In this mechanism, traffic control parameters and VMS parameters are optimized together. Particularly for arterial signal control, a twostage nested optimization problem is formulated. To find the optimal solution, we apply a simulationbased optimization framework for solving this twostage nested optimization problem. In this cooperative strategy, we allow the communication between VMS and TSC systems to find a desirable and feasible solution during each control interval.
As shown in Table
Besides, it should be pointed out that this simulationbasedoptimization method requires relatively high computation source by utilizing microscopic traffic simulation. For future works, we will focus on testing the proposed method for more complex networks of intersecting arterials to investigate effectiveness of the cooperative strategy. In addition, analytical methods will be researched in this cooperative mechanism to reduce computation costs.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by HiTech Research and Development Program of China under Grant 2014BAG03B04, National Natural Science Foundation of China Grant 51138003, and National Science and Technology Major Project 2012ZX03005016002.