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Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

In this paper, an extended car-following model is proposed to simulate traffic flow by considering the honk effect. The stability condition of this model is obtained by using the linear stability analysis. The phase diagram shows that the honk effect plays an important role in improving the stabilization of traffic system. The mKdV equation near the critical point is derived to describe the evolution properties of traffic density waves by applying the reductive perturbation method. Furthermore, the numerical simulation is carried out to validate the analytical results and indicates that the traffic jam can be suppressed efficiently via taking into account the honk effect.

The phenomenon of passing on a two-dimensional network has been studied through lattice hydrodynamic approach. Near the critical point, the effect of passing is investigated theoretically and numerically. The modified Korteweg–de Vries equation near the critical point is derived using the reduction perturbation method through nonlinear analysis. Analytically, it is shown that for all possible configurations of vehicle, the stable region significantly reduces with an increase in the passing rate. It is shown that the jamming transition occurs among no jam to chaotic jam for any configuration of vehicles for larger rate of passing constant, while for smaller rate of passing, the jamming transitions occur from no jam to chaotic jam through kink jam for any configuration of vehicles. The results show that the modified model is able to explain the complex phenomena of traffic flow at a better level of accuracy than the most of the existing models. Simulation results are found consistent with the theoretical findings, which confirm that the passing plays a significant role in a two-dimensional traffic system.

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance.

In this paper, a new car-following model of traffic flow is proposed by considering the driver’s individual anticipation behavior (forecast behavior and response delay behavior). The effect of anticipation parameters corresponding to the individual differences upon the stability of traffic flow is examined through linear stability analysis. The simulation results are in good agreement with the theoretical analysis and confirm that traffic flow can be stabilized efficiently by drivers’ forecast effect (positive anticipation parameter) and, nevertheless, be destabilized by drivers’ physical delay effect (negative anticipation parameter). Thus, the two aspects of drivers’ individual anticipation behavior play an important role in stabilizing or destabilizing the traffic system, respectively.

The drivers in actual traffic differ in the aggressiveness in driving behaviors, which will finally be reflected in the interaction between vehicles and fluctuations in flows. In this paper, we try to study the effect of driving aggressiveness on the traffic stability by proposing an extended microscopic car-following model, in which the optimal velocity is reconstructed to divide the drivers in traffic system into two groups according to driving aggressiveness of each individual. The stability condition of the proposed model is derived to explore its ability against a small perturbation by use of the linear stability theory. We obtain the neutral stability lines for different percentages of drivers with a higher driving aggressiveness, finding that the traffic flow trends to stable with the increase in the percentage for higher driving aggressiveness drivers when the average headway is less than a critical value or greater than another critical value, but when the average headway falls into the intermediate range between the two critical values, the traffic flow becomes more and more unstable with increase in the percentage of drivers with a higher driving aggressiveness. Finally, numerical simulations are conducted to verify these theoretical results and examine how the percentage of vehicles driven by higher driving aggressiveness drivers affects the traffic flux of the vehicle system.

Recent years, multimodal vehicles consisting of regular vehicles, CVs (connected vehicles) and AVs (automated vehicles) develop a persistent multimodal traffic streams, which changes the traffic composition of urban road network. The gradually proven automatic driving technique fastens the participation of Automatic Vehicles (AV) in real traffic system, and changes the environment, objective characteristics and possible methods of traffic control. This paper established a mathematical model of continuous traffic flow under two kinds of rules in simulation technologies according to improved cellular automata theory, and analyzed the performance of two kinds of rules through reasonable assumptions and simplifications and taking into account the efficiency, safety and applicability. In addition, this research analyzed the impact of automatic vehicles on the performance through assigning a zero value and used randomization deceleration probability to describe the uncertainty psychological factors of drivers and drivers' reaction time. This research found that within the impact of automated driving environment, continuous traffic flow will have higher efficiency and safety by adopting the fast and slow lanes classification rule.

With the continuous breakthroughs of unmanned vehicle technology, a increasing number of unmanned vehicles will be added to the current traffic system. How will the above situation influence the traffic, and how will we change the existing traffic system design program to help unmanned technology to improve people's travel efficiency in a better way, have become urgent problems to be solved. In this paper, the model of Queuing Theory is applied to simulate and calculate the number of lanes of different toll stations, on which basis, the optimal proportional adjustment model of ETC (Electronic Toll Collection) is obtained. Finally, the results of sensitivity analysis of the model show that it has an excellent robustness and reliability.

For the purpose of alleviating traffic congestion, this paper proposes a scheme to encourage travelers to carpool by traffic restriction. By a variational inequity we describe travelers’ mode (solo driving and carpooling) and route choice under user equilibrium principle in the context of fixed demand and detect the performance of a simple network with various restriction links, restriction proportions, and carpooling costs. Then the optimal traffic restriction scheme aiming at minimal total travel cost is designed through a bilevel program and applied to a Sioux Fall network example with genetic algorithm. According to various requirements, optimal restriction regions and proportions for restricted automobiles are captured. From the results it is found that traffic restriction scheme is possible to enhance carpooling and alleviate congestion. However, higher carpooling demand is not always helpful to the whole network. The topology of network, OD demand, and carpooling cost are included in the factors influencing the performance of the traffic system.

The deterioration of the performance of the traffic system through the ACC-vehicles discussed in Chap. 6 could be avoided through the use of automatic driving systems in vehicles, which learn from behaviors of drivers in real traffic as incorporated in hypotheses of the three-phase theory.

The paper presents the simulation study results of the traffic system in the city of Hlohovec. The authors describe the process of building the model in Witness 2013c simulator. The individual entities of simulation model are explained in details, as the Witness simulator is primarily suitable for manufacture or service simulation and not for traffic system simulation. The goal of this paper is to analyse the traffic system and to suggest the changes for improving the traffic in the city of Hlohovec. All proposed measures will bring about 20% improvement of traffic when compared to the current state.

Post evaluation is necessary for assess the success degree of a new system. A vessel traffic system project post evaluation method based on cloud model is presented. Firstly, we created the post evaluation index system and weight; Secondly, the expert scored the indexes and then cloud models were achieved; Thirdly, the comprehensive cloud algorithm calculated the result; Finally, the forward cloud generator restore the droplets distribution. The experimental result shows that the method is effective and intuitive.

Traffic simulation, a powerful scientific tool, can be applied to both transportation planning and to transportation design and operations. In this paper, a micro-simulation model is developed to simulate the behavior of individual vehicles on the freeway and is used to evaluate the impact of changes in efficiency and safety resulting from changes to traffic flow and speed limits. All aforementioned influences are expressed at a quantitive level.

Taking the four-aspect fixed autoblock system as research object, this work proposes the cellular automata model of the fixed autoblock system based on existing theoretical researches through the analytical study of classical cellular automata model. The complexity and openness of the railway system are combined with the characteristics of cellular automata in this model. Through simulation, this paper studies and obtains the rules of the effect of redundant time on the operation of passenger and freight traffic system.

To establish intelligent traffic system,using better traffic flow monitoring methods is its primary task, in the paper proceed from the actual situation, proposed several methods such as based on the ground sense coil monitoring, ultrasonic monitoring and video image monitoring, and compare the advantages and disadvantages of the three methods, finally, applying these methods to a reasonable application of occasions.

Deceleration has a great influence on the whole traffic flow in real traffic. Based on the SDNS cellular automaton (CA) traffic model, an improved single lane traffic model with considering the safe deceleration was proposed. The model not only simulated the interaction between vehicles, but also reproduced nonlinear phenomena which tallies with real traffic such as the synchronism flow and the metastable state. The model in this paper can reduce some vehicles to decelerate. Simulation is shown that the smaller thepis, the more remarkable the variation of the proportion of the vehicle is in the early stage.

The current constructional project have significant influence on the traffic system in the evaluation scope by TIA, the modification of the regulatory detailed plan and the plan conditions proposed is impossible for its characteristic of ex-post evaluation. Thus the effectiveness of TIA is greatly reduced. This paper discusses this issue and puts part of the content of TIA upward into regulatory plan stage. The focus and content of each stage can be clearly assigned by the integration of comprehensive transportation system plan in master plan, TIA in regulatory plan, TIA in constructional detail plan and project design. Furthermore, the paper introduces the measure which was put forward in urban comprehensive transportation plan of Bayuquan District in Yingkou City and TIA was applied in regulatory plan of Wanghai new District. By improving the method and testing the effects, the role of TIA can be further enhanced.

The merge control models proposed for work zones are classified into two types (Hard Control Merge (HCM) model and Soft Control Merge (SCM) model) according to their own control intensity and are compared with a new model, called Individual Subjective Initiative Merge (ISIM) model, which is based on the linear lane-changing probability strategy in the merging area. The attention of this paper is paid to the positive impact of the individual subjective initiative for the whole traffic system. Three models (ISIM, HCM, and SCM) are established and compared with each other by two order parameters, that is, system output and average vehicle travel time. Finally, numerical results show that both ISIM and SCM perform better than HCM. Compared with SCM, the output of ISIM is 20 vehicles per hour higher under the symmetric input condition and is more stable under the asymmetric input condition. Meanwhile, the average travel time of ISIM is 2000 time steps less under the oversaturated input condition.

Charging resources in the charging stations are limited. How to make electric vehicles get charging service with small cost is worth studying. The reserving charging model for electric vehicles is built based on the customer requirement model, considering the road condition of traffic system and available charging resources in the charging stations. All factors that influence the reserving charging are translated into weights and then being assigned to corresponding edges of traffic network graph. The Dijkstra algorithm is introduced to achieve the solution of the model built in this paper. Finally, a supposed traffic network is used to conduct a simulation, and the results show that the proposed optimization model of reserving charging for electric vehicles can satisfy users' different demands.

Since the number of vehicles has been increasing and adding more complexity to the transportation infrastructure every day, safety and efficiency in transportation system are of utmost importance. Effective communication between vehicles and with traffic infrastructures (e.g. traffic signs, traffic lights, etc.) play a key role to ensure a safe and secure traffic system. Visible Light Communication being a very promising technology, can be an ideal solution to address the challenges in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. A fundamental challenge in this regard is the design of a proper sensor to minimize the interference of the ambient light in daytime. Optical filters can be used to minimize the problem, but additional techniques are required to further optimization. In this paper, we discuss the prospect of using a differential optical receiver in vehicular VLC communication, which can cancel ambient light and other atmospheric noises.

Urban traffic network is a typical example of a complex system called hybrid dynamic system (SDH). Although hybrid automata (HA) is a popular formalism in modeling, analysis and controlling SDH, the HA model of large scale urban traffic network gets substantially complicated for analysis and control due to large dimensionality of such systems. To overcome this problem, a new hierarchical control structure for large network based on HA is investigated in this paper. This technique divides the complex control problem of a large traffic system into different levels. The proposed control technique has been tested for an urban traffic network of four intersections under different traffic conditions. The experimental results are compared with those from Synchro (version 8). Simulation results affirm the improvement and the effectiveness of the proposed approach.

The station-less shared traffic system removes the restriction on rental and return at the predetermined stations. How to minimize the cost of vehicle maintenance and dispatch remains a big concern. This paper proposes a genetic algorithm based on the features of station-less shared traffic system to optimize the multi-objective problem to achieve minimized system deviation and cost, by redesigning coding method and fitness function. We took it into practice in the operation of smart cars in Zijingang Campus of Zhejiang University, making a schema which has the minimum deviation 6.2 and has at least 17.29% cost cut over other schemas having the same deviation.

On the basis of analyzing and abstraction of the relationships of all factors in road traffic system to driver’s personal ability and the requirements of systematic task, the states of driver’s personal ability and systematic task are researched. The safety level under different match states is revealed and the matching criterion for higher safety properties is provided through studying of the match level of driver’s personal ability and systematic task. In addition, the methods how to examine drive’s ability or system state.

In this paper, a new method is presented to optimize the elevator traffic system based on queue model. The application of this method is studied in detail. Parameters for the elevator running including number of elevator passenger, average waiting time and arriving ratio, and the relationships between them have been analyzed as well. Therefore, the dispatching of elevator dynamic partition can be done. The actual data of rush hours are used for simulation tests. The simulation results show that this method has good performance. The method is a more practical control strategy especially in rush hours.

To evaluate the regularity strength of elevator traffic (RSET) is the prerequisites to efficiently design scheduling strategies of elevator group control system (EGCS). In view of the characteristics of the uncertainty of elevator traffic, a quantitative method to evaluate RSET is proposed by estimating the confidence interval of 5min passenger arrival rate on observed data, which provides important support for forecasting elevator traffic and adjusting elevator group scheduling strategies. Simulation results of an applied example show the validity of the proposed method.

The road capacity can be greatly improved if an appropriate and effective information feedback strategy is adopted in the traffic system. In this paper, a strategy called piecewise function feedback strategy (PFFS) is introduced and applied into an asymmetrical two-route scenario with a speed limit bottleneck in which the dynamic information can be generated and displayed on the information board to guide road users to make a choice. Meanwhile, the velocity-dependent randomization (VDR) mechanism is adopted which can better reflect the dynamic behavior of vehicles in the system than NS mechanism. Simulation results adopting PFFS have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the previous strategies.

Based on the driver’s individual difference of the driver’s perception ability, we in this paper develop a new fundamental diagram with the driver’s perceived error and speed deviation difference. The analytical and numerical results show that the speed-density and flow-density data are divided into three prominent regions. In the first region, the speed-density and flow-density data are scattered around the equilibrium speed-density and flow-density curves of the classical fundamental diagram theory, where the widths of these scattered data are very narrow and slightly increase with the real density (i.e., the scattered data appear as two thicker lines); the running speed is approximately equal to the free flow speed and the real flow approximately linearly increases with the real density. In the second region, the speed-density and flow-density data are scattered widely in a two-dimensional region, but the shapes of these widely scattered data are related to the properties of the driver’s perceived error and speed deviation difference. In the third region, the scattered speed-density and flow-density data appear but these scattered data will quickly degenerate into the equilibrium speed-density and flow-density curves with the increase of the real density. Finally, the numerical results illustrate that the new fundamental diagram theory also produces the F-line, U-line, and L-line. The shapes of the scattered data, F-line, U-line, and L-line are relevant to the properties of the driver’s perceived error and speed deviation difference. These results are qualitatively accordant with the real traffic, which shows that the new fundamental diagram theory can better describe some complex traffic phenomena in the real traffic system. In addition, the above results can help us to further explain why the widely scattered speed-density and flow-density data appear in the real traffic system and better understand the effects of the driver’s individual difference on traffic flow.

Despite the rule that cyclists must ride on the right half of the road is written into the state vehicle code, the phenomenon of riding against the bicycle flow is still serious. To investigate the effect of bicycles going in the wrong direction, a Bi-Directional Adaptive EBCA model is developed in this paper. The phase transition F-J as well as the phase transition F-S-F are suggested by observing the spatial-temporal pattern. The deterministic case that the linear relationship between the average flow rate and the bicycle number disappears when the average density exceeds a particular value is shown. Under the stochastic case, the impacts of the avoiding probability Ps and the returning probability Pr on the traffic system are analyzed. The results of the simulation are in good agreement with the realistic bicycle flow.

The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this paper, we study dynamics of traffic flow with real-time information. The influence of a feedback strategy named vehicle number feedback strategy (VNFS) is introduced, in which we only calculate the vehicle number of first 500 route sites from the entrance. Moreover, the two-route traffic system has only one entrance and one exit, which is different from those in the previous work. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow, and simulation results by adopting this optimal information feedback strategy have demonstrated higher efficiency in controlling spatial distribution of traffic patterns than the other three information feedback strategies, i.e., TTFS, MVFS and CCFS.

This paper investigates traffic dynamics of two-lane mixed traffic flow system composed of cars and buses, which are characterized by different lengths and different maximum velocities. Four lane changing regulations are studied, which reveals effect of lane changing ban, symmetric and asymmetric lane changing rules on traffic flow characteristics (flow rate, carry capability, lane changing frequency, and lane usage). We expect that our results could be useful for traffic management.

This paper presents numerical experimental results for testing a simulation-based combined dynamic traffic assignment and signal optimisation algorithm under incident conditions. The algorithm utilises a path-based signal optimisation procedure, which uses vehicle paths information from a dynamic traffic system optimal assignment model to optimise the signals. The use of vehicle path in the signal optimisation procedure makes it much easier to interact with the path-based dynamic traffic assignment models. The algorithm is implemented and tested under normal and incident conditions. The experiments consist of implementations of the solution algorithm under different congestion levels for the test networks. Some experiments, under incident conditions for the test network, were used to examine the robustness of the solution algorithm. The algorithm is shown to reach convergence in all experiments, and to result in significant improvements over the system optimal assignment solution under different congestion levels.

We show that non-Poisson and Poisson processes can coexist in ordered parallel multilane pedestrian traffic, in the presence of lane switching which asymmetrically benefits the switchers and nonswitchers. Pedestrians join at the tail end of a queue and transact at the opposite front end. Their aim is to complete a transaction within the shortest possible time, and they can transfer to a shorter queue with probability ps. Traffic is described by the utilization parameter U = λ〈ts〉/N, where λ is the average rate of pedestrians entering the system, 〈ts〉 is the average transaction time, and N is the number of lanes. Using an agent-based model, we investigate the dependence of the average completion time 〈tc〉 with variable K = 1 + (1 − U)−1 for different N and 〈ts〉 values. In the absence of switching (ps = 0), we found that 〈tc〉 ∝ Kτ, where τ ≈ 1 regardless of N and 〈ts〉. Lane switching (ps = 1) reduces 〈tc〉 for a given K, but its characteristic dependence with K differs for nonswitchers and switchers in the same traffic system. For the nonswitchers, 〈tc〉 ∝ Kτ, where τ < 1. At low K values, switchers have a larger 〈tc〉 that also increases more rapidly with K. At large K, the increase rates become equal for both. For nonswitchers, the possible tc values obey an exponentially decaying probability density function p(tc). The switchers on the other hand, are described by a fat-tailed p(tc) implying that a few are penalized with tc values that are considerably longer than any of those experienced by nonswitchers. © 2006 Wiley Periodicals, Inc. Complexity 11: 35–42, 2006

The behavior of traffic system controlled by traffic lights is presented under assumptions of city traffic on a single lane based on the optimal velocity model. The effect of differrent traffic light control strategies on the traffic flow is discussed using three different strategies, i.e. the synchronized, green wave, and random offset strategies. The flow-density diagrams are analyzed using these strategies. The numerical investigation is carried out using cellular automata model to get a better understanding of the microscopic behavior of the system under the three different traffic light strategies.

In real traffic, the traffic system is usually composed of different types of vehicles, which have different parameters. How these parameters, especially the lengths of the vehicles, influence the traffic behaviors and transportation capability has seldom been investigated. In this paper, we study the mixed traffic system using the cellular automata traffic flow model. The simulation results show that when the road occupancy rate is large, increasing the fraction of long vehicles can apparently, improve the transportation capability. The influence of slow vehicles fraction on the average velocity of vehicles has been discussed, and it is found that the influences are very different when the difference of vehicle length is considered or not.

This paper extends a recently proposed single-lane cellular automaton model [Li et al., Phys. Rev. E64, 066128 (2001)], which considers the velocity effect of the preceding car, to two-lane traffic system. The traffic behaviors in both homogeneous system and inhomogeneous system are investigated. For homogeneous traffic, it is shown that the velocity effect enhances the maximum flux but does not change the qualitative properties of the fundamental diagram. Nevertheless, the qualitative changes of the lane changing frequency and congested pattern occur. In the inhomogeneous system, the honk effect is studied. It is found that the honk effect first strengthens then weakens with the increase of R, the ratio of slow cars to all cars.

Self-Organising Traffic Lights (SOTL) are considered a promising instrument for the development of more adaptive traffic system. In the current paper we explain why some well promoted results obtained with the use of SOTL should be scrutinised and carefully reviewed. Currently undergoing research computational projects based on SOTL should be reviewed too.

ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT) is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEA's parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.

The traffic state of the urban road network is determined by travelers’ choices of travel modes and routes. With the development of science and technology, people tend to have more travel choices and their distinctive temperaments often lead to different choices even in the same situation. Therefore, a study of different factors that may affect people’s travel choices plays a crucial role in the optimization of the traffic system. Focusing on the four major travel modes between Minhang campus and Xuhui campus of Shanghai Jiao Tong University (SJTU) in Shanghai, China, this paper tries to gather the information of the factors that affect travel choices and the extent of such effects both in general cases and when prior information is given by means of questionnaires. Based on data processing, the paper draws pie charts on the travel choices under different circumstances and makes a qualitative analysis of the influential factors. Then, a quantitative analysis is made by using the models of utility function and linear programming. Finally, in contrast with the results, the paper finds out the extent of the effect of travel information on the choice of travel modes and routes of the travelers with different temperaments.

In this paper, we describe an approach of modeling and simulation based on multi-agent theory to build the model of traffic system. Our model presents a traffic system including traffic light system at a ‘+’ junction. This paper controls duration of red-light/green-light to release traffic congestion on roads. In addition, we establish six Vietnamese traffic rules in our model. The simulation model represents real traffic system in GAMA platform with GAML language. All systems are visualization in GUI to show the participant of vehicles and traffic light on roads.

We propose a cyber-physical platform that combines road traffic simulation, network simulation, and physically simulated vehicles to facilitate extensive testing on various levels of vehicular systems. Our design integrates physical and digital vehicle simulation into a common development and testing environment. This paper describes the platform design and presents prototypical implementations that use Simulator of Urban Mobility (SUMO), TinyOS Simulator (TOSSIM), a 3D sensor simulation environment, and a test-bed of miniature vehicles called Gulliver. As a prototypical implementation, we demonstrate the development of cooperative applications, and by that we achieve: (a) a cyber-physical system that provides a common environment for physically and digitally simulated vehicles, (b) a platform to interface communication between physically and digitally simulated vehicles, and (c) the ability to tailor testing scenarios in which some system components are simulated digitally and some physically.

The car-following model is extended to take into account the characteristics of mixed traffic flow containing fast and slow vehicles. We conduct the linear stability analysis to the extended model with finding that the traffic flow can be stabilized with the increase of the percentage of the slow vehicle. It also can be concluded that the stabilization of the traffic flow closely depends on not only the average value of two maximum velocities characterizing two vehicle types, but also the standard deviation of the maximum velocities among all vehicles, when the percentage of the slow vehicles is the same as that of the fast ones. With increase of the average maximum velocity, the traffic flow becomes more and more unstable, while the increase of the standard deviation takes negative effect in stabilizing the traffic system. The direct numerical results are in good agreement with those of theoretical analysis. Moreover, the relation between the flux and the traffic density is investigated to simulate the effects of the percentage of slow vehicles on traffic flux in the whole density regions.

Inter-cognitive communication plays the key role in the development of engineering applications where natural and artificial cognitive systems should work together efficiently. Situation-awareness is essential in that cooperation. The focus of this study is to report our experiences and applications of situation-aware robots and transporters. Industrial robots are an important part of production in several fields of current industry. Mobile robots and wheeled transporters such as forklift trucks will in the future be key elements in material handling and transporting tasks in production and in warehouses. In addition, robots will also play key roles in wellness services in response to the aging population. Situation-awareness and especially location-awareness is essential in the development of efficient human-robot interaction. In the future, cognitive communication processes between operators and intelligent transporters may benefit from many features developed for intelligent traffic systems. We also present our experiences from the development a of situation-aware traffic system.

A car split rate model based on Strategic Transport Model (STM) method has been proposed in this paper. The model can be used to predict the car split rate in urban traffic system. It also allows assessing the consequences of strategic changes in urban traffic system. With in-depth study of the strategic transport model, the SCGC model (Service Characteristics based General Cost model) is built. Then taking service characteristics such as economic cost, time cost, service accessibility and comfort factor into consideration, the split rate model for private car based on the STM is proposed. With the residents travel survey data from 2005 to 2007 in Beijing city, the models are calibrated. According to the parameters the model is validated and the relative error between actual data and the prediction data is 2.47%, the prediction result is of high accuracy. It is proved that the model is accurate and serviceable.

This paper discusses Dynamic Traffic Cellular Automata (DTCA) model. It integrates acceleration, deceleration, and reaction probability functions. The effects of these probability functions on the traffic flow has been explained and shown that they have a major impact on the prediction of traffic flow. This mathematical approach has been verified by simulations.

Although more attention has been attracted to benefit evaluation of Intelligent Transportation Systems (ITS) deployment, how ITS impact the traffic system and make great effects is little considered. As a subsystem of ITS, in this paper, Intelligent Transportation Management System (ITMS) is studied with its impact mechanism on the road traffic system. Firstly, the correlative factors between ITMS and the road traffic system are presented and 3 positive feedback chains are defined. Secondly, we introduce the theory of Fundamental Diagram (FD) and traffic system entropy to demonstrate the correlative relationship between ITMS and feedback chains. The analyzed results show that ITMS, as a negative feedback factor, has damping functions on the coupling relationship of all 3 positive feedback chains. It indicates that with its deployment in Beijing, ITMS has impacted the improvement of efficiency and safety for the road traffic system. Finally, related benefits brought by ITMS are presented corresponding to the correlative factors, and effect standards are identified for evaluating ITMS comprehensive benefits.

Under the real circumstances of traffic flow's grey features and traffic system's delay effect, this paper construct a grey delay model (GM (1,1,τ) model) and investigate relevant properties, Finally, we complete a traffic experiment on the section of Youyi Avenue, estimate the delay time of the system and establish the delay model. The comparison between the delay model with the GM(1,1) model without considering practical conditions of the traffic system shows that incorporating existing delay effect of the traffic system into the model can reasonably reflect the characteristics of the system and provide satisfactory predictions.

Control and evaluation of transportation and traffic system by using simulation and combining with optimization method are significant researches. By means of this research method, a real transport system mixed into a traffic system is supervised and optimized; and the influences among varied vehicle flows are determined. The method of combining simulation with optimization and process of applying simulation to transportation and traffic system are discussed.

In this paper, we propose a model for vehicle traffic based on multi-agent systems and account suppositions and its issues. Traffic is an ever-growing problem as population and the number of drivers around the world increase exponentially. Previously, fluid flow models have been used in an attempt to model traffic. Based on recent studies, only agent based models can accurately model a traffic scenario. This is because small perturbations could have a butterfly-like effect, which causes a rapid change in the entire system.