The authors argue that travel forecasting models should be dynamic and disaggregate in their representation of demand, supply, and supply-demand interactions, and propose a framework for such models. The proposed framework consists of disaggregate activity-based representation of travel choices of individual motorists on the demand side integrated with disaggregate dynamic modeling of network performance, through vehicle-based traffic simulation models on the supply side. The demand model generates individual members of the population and assigns to them socioeconomic characteristics. The generated motorists maintain these characteristics when they are loaded on the network by the supply model. In an equilibrium setting, the framework lends itself to a fixed-point formulation to represent and resolve demand-supply interactions. The paper discusses some of the remaining development challenges and presents an example of an existing travel forecasting model system that incorporates many of the proposed elements.

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority (TSP) strategy that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSIM (VISual SIMulation) simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.

Underground urban expressways are a possibility for solving many existing transportation-related problems, such as traffic congestion in high density areas and the division of neighborhoods due to elevated roadways. However, they may also pose high risks regarding traffic safety. Therefore, it is important for a pre-analysis of traffic safety to be made. This paper describes recent efforts to develop a driving simulation system, MOVIC-T4, for traffic safety analysis of underground urban expressways. In order to develop a small portable simulator, a small-sized motion-base with two-degrees-of-freedom is used to duplicate acceleration cueing together with a head-mounted-display (HMD) for the visual system. An overview of this system is given and the reliability of driving data obtained from the experiments using MOVIC-T4 is discussed through a validation study using field driving data. The results of validation indicate that the perceived speed, distance headway, and physiological data in the simulator show the almost same trend as that in the real world, but larger decelerations tend to be produced in the simulator.

Road pricing policies have two equally important objectives that have to be considered: the optimal use of the road network and the fiscal sustainability for the management of the network. The aim of this work is to examine the effects on these two objectives of a recent pricing policy implemented in Japan's expressway networks. Recently the Nippon (Japan) Expressway Companies (NEXCO, operators of Japan's nation wide expressway network) have implemented several toll discount policies for users equipped with electronic toll collectors (ETCs). One of the policies is the commuter discount policy that discounts 50% for a journey on the NEXCO expressway no longer than 100 km during the commuting peak hours. This paper examines its effect on congestion reduction in the road network of Nagoya area and its influence on the use of expressways operated by another major local public expressway company in Nagoya. The congestion easing effect of this policy is compared with a potential marginal cost pricing scheme. These observations suggest that policy coordination regarding toll levels is needed for expressway corporations and transportation authorities for improving transportation efficiency of utilization of the whole network.

This paper presents a network equilibrium formulation for modeling commuters' travel choices in a bimodal transport system with park-and-ride (P&R) trips while the total demand is elastic to the congestion level of the network. A super-network approach is adopted in the proposed model. It is assumed that commuters' trips are categorized into two types, auto mode only and a combined mode with both auto and transit modes. The former is referred to as the pure mode trip and the latter as the P&R mode trip. The proposed model simultaneously considers the commuter's choice of the pure mode versus the P&R mode, the choice of parking location for the pure mode, the choice of transfer point for the P&R mode, as well as the route choice for each mode. The demand elasticity of transport system, the capacity constraints of transport facilities, and the congestion interaction throughout the super-network are also explicitly incorporated into the proposed model. The results of the numerical experiment show the following key findings: (i) traditional parking/P&R models may overestimate or underestimate travel demand distribution over network; (ii) parking/P&R, transit scheduling, and carpooling schemes bring significant impacts on commuters' travel behavior and network performance; and (iii) different transport policies may be to some extent mutually substituted.

The optimization of parking charges and parking supply over the time of a day is an important problem in the design of transportation networks. This paper presents a bilevel model to determine the optimal time-varying parking charges and parking supply in road networks with multiple user classes and different types of parking facilities. The upper level of the model aims to maximize the network net benefit in response to the parking charges and parking supply, whereas the lower level is a time-dependent network equilibrium problem with elastic demand. A descent-gradient-based solution algorithm is adapted to solve the model. The numerical results show that the implementation of time-varying parking charges and parking supply is useful to effectively cater to the time-varying demand with different parking needs. The model provides a powerful tool for strategically designing parking locations and evaluating various parking policies.

An analytical model is presented to estimate traffic pollutant concentrations based on an artificial neural network (ANN) approach. The model can analyze the highly nonlinear relationship between the traffic flow attributes, meteorological conditions, road spatial characteristics, and the traffic pollutant concentrations. This study analyzes the multiple factors that affect the pollutant concentration and establishes the model structure using the ANN technique. Collected data for the pollutant concentrations as functions of variant factors was used to train the ANN model. A method was developed to automatically measure the traffic flow attributes, such as traffic flow, vehicle speed, and flow composition from video data. The results indicate that the model can reliably forecast CO₂ concentrations along the roads.

In this paper we describe how the capacitated user equilibrium can be approximated by sequential uncapacitated models by the use of a penalty function. The efficiency of the method is governed by the algorithmic performance of the uncapacitated model. A skew gradient-based Newton method is used to solve the capacitated user equilibrium within the feasible region of path flows. In the path-flow region, the straight gradient is defined as the derivative of the objective function with respect to the flow of the corresponding path, while the skew gradient is defined for each particular origin destination pair and is characterized by the average cost of all the paths for that pair. Instead of movement of flow toward the shortest path, in the equilibration procedure path flows below the average decrease and path flows above the average increase. The characteristics of the Newton method with the column generation procedure are combined to achieve the efficient determination of the equilibrium point. Numerical experiments demonstrate the excellent performance of the proposed method and highlight its potential applications.

Effective use of urban rapid railway systems requires that the railway systems be effectively connected with other transportation modes so that they are accessible. This paper uses the logit model and data to analyze the factors influencing railway access choices in a railway choice access model. The results indicate that access time, access cost, and access distance are factors significantly affecting railway access choices. The user's income significantly affects the probability of choosing to walk rather than to take a taxi, but is not related to choosing buses or bicycles. Vehicle ownership significantly affects the probability of choosing a taxi, but is not significantly related to the other modes. The conclusions provide an analysis tool for urban railway planning and construction.

Bicycle traffic has a significant effect on the capacity of signalized intersections. This paper divides the influence of bicyclists on vehicular flow into four types with the time durations estimated based on probability, shock wave, and gap acceptance theory. Vehicular saturation flow rate is predicted for various conditions on the basis of the speed-flow curve for the capacity of typical intersections influenced by bicycle traffic. The model overcomes the limitations of the Highway Capacity Manual (HCM, 2000) method for left-turns due to data collection, and takes into account the effect of trapped bicycles on the through vehicular traffic. The numerical results show that the left-turn and through capacities predicted by the model are lower than those of the HCM method. The right-turn capacity is close to that of the HCM method at low bicycle volumes and higher than that of the HCM method at high bicycle volumes.

The theory of complex networks was used to classify public transport networks into public transportation route networks, public transportation transfer networks, and bus station networks. The practical significance of the network parameters was then analyzed. The public transport networks in Langfang, Jining, and Dalian were then chosen as specific research cases. The results show that the public transportation networks have the characteristics of complex networks. In addition, the urban transportation network parameters all significantly affect the accessibility, convenience, and terrorist security capability of the urban public transportation network. The results link the findings with the actual situations to explore means to solve transportation system problems.

The cellular automata (CA) micro-simulation model was used to describe the behavior of the mixed traffic flows at crosswalks where the pedestrians compete with the vehicles to cross the roadway. The focus of this paper is the behavior of pedestrians and the influence of pedestrians' behavior on the vehicle flow, pedestrian flows, and the vehicle waiting time. The proportion of pedestrians who do not obey traffic laws, the group effect, and expected waiting time of pedestrians, regarded as the most important pedestrian characteristics, are taken into consideration in the analysis. Simulation results show the ability of the micro-simulation to capture the most important features of mixed traffic flow.

Biomass fuels have become a big concern due to the large increase in green house gases and the rapid rise of petroleum prices around the world. This paper reviews recent developments in biomass fuels, such as ethanol and biodiesel, in China. Ethanol-gasoline mixture (E10) for vehicles is currently distributed in nine provinces while biodiesel is under development. One way to extend the application of ethanol is to burn it in diesel engines to lower soot emissions. The effects of the different methods blending ethanol with fossil diesel, and blending biodiesel with fossil diesel and ethanol-diesel on the combustion and emissions are investigated. The test results show that ethanol and biodiesel can be mixed with fossil diesel to greatly reduce particulate matter and soot emissions from diesel engines. But the application of ethanol blending with fossil diesel is more difficult than that of ethanol blending with gasoline, and biodiesel blending with fossil diesel. The dual-fuel injection of ethanol and diesel systems has the highest smoke reduction effect for a high ethanol fraction.

State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints. However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control.