Vehicle Headway Research Articles

Travel behavior analysis using smart card data

Electronic fare payment systems have gained much popularity in large public transport systems across the world. Though their main purpose is to collect the revenue and improve passenger's comfort, they also generate huge amount of detailed and accurate data on onboard transactions which is of extreme importance for transit planners for efficient short-term and long-term transit operation and service planning. This paper attempts to utilize the smart card data as an input for large-scale activity based public transport simulation for analyzing travel behavior pertaining to transit users, using an open source agent-based transport simulation package, MATSim. Transit vehicles are optimized based on input demand generated by smart card and a high temporal resolution of waiting time and its components at different stations is extracted which is very important in managing the frequency and headway of transit vehicles. Since no card swipe is needed for transfers within subway system in Seoul, the data is not readily available to detect frequent transfer locations. However, after validating the boarding and alighting volumes at different stations, a methodology is presented to calculate within subway transfer volumes at different stations. We suggest that by improving the statistical analysis and utilizing advanced data mining techniques, smart cards can effectively generate the microsimulation travel demand models.

Discharge Headway Model for Heterogeneous Traffic Conditions

Discharge headway is the headway between successive vehicles negotiating an intersection during the green time of signal oper- ation. It is an important parameter in signal operations and analysis since estimation of parameters such as saturation flow and capacity of an intersection depend on it. Although there have been several studies on discharge headway in homogeneous traffic conditions, there are only a few studies on discharge headways in heterogeneous traffic. This study examines the factors affecting discharge headway under heterogeneous traffic conditions which is characterized by mixed vehicle composition and lack of lane discipline. A novel method to measure headways in such cases is proposed here.To get individual vehicle headways, each lane is divided into multiple strips. The width of a strip is approximately equal to the width occupied by a motorcycle. The headways of vehicles in each strip are measured separately and used for analysis. Data collection for the study was carried out at signalized intersections in Chennai, India. Data was collected for one approach at all intersections. From the data collected, headways of individual vehicles were measured. Linear mixed effect regression was used to model discharge headway. The effect of vehicle type, lateral position on roadway, and green time on discharge headway were modeled. From the regression analysis, it was found that all these factors had significant impact on discharge headway. The discharge headway model proposed in this study could be used for obtaining saturation flow rates and capacity at signalized intersections under heterogeneous traffic conditions.

Analysis of evolution mechanism of traffic energy dissipation by considering driver’s forecast effect

Starting from the full velocity difference model, an extended car-following model is proposed by considering the influence that in real traffic the driver’s forecast has an effect on car-following behavior of traffic flow. The mechanism how the stability and energy dissipation of traffic flow are in fluenced by the driver’s forecast effect is revealed by the application of the proposed new model. The linear stability condition of the new model is derived theoretically through linear stability theory. The phase diagram of linear stability condition is divided into two regions by each stability curve: the stable and unstable regions. And the corresponding stable region will be enlarged with the increase of driver’s forecast time, hence the traffic condition will be improved through considering driver’s forecast effect. By numerical simulation method, the space-time evolution relation between the velocity and headway of vehicles in car-following queue is investigated systematically under the influence of driver’s forecast. In the same time, the evolution mechanisms of the overall average energy dissipation of traffic flow and individual vehicle energy consumption with the addition of small disturbance are discussed explicitly under a periodic condition, and it is discovered that the overall average energy consumption in traffic flow and the energy dissipation of individual vehicle is accompanied by a complex critical phase transition process. Good agreement between the numerical simulation and the theoretical analysis show that by considering of driver’s forecast effect, not only the stability of traffic flow is enhanced obviously, but the energy consumption is reduced remarkably as we expect. Furthermore, it is verified that both the overall average energy consumption of the considered traffic flow and the energy consumption of an individual vehicle are reduced gradually along with the increase of driver’s forecast time. On the other hand, numerical simulation results verify that the shortcoming of negative speed appearing in the full velocity difference model with low reaction coefficient can be effectively avoided by increasing the driver’s forecast time in the improved model, which means that the dynamic characteristics of traffic flow can be described more precisely by the proposed model.

One-dimensional cellular automaton model of traffic flow considering dynamic headway

Based on the NaSch cellular automaton traffic model, a modified single lane traffic model is proposed by considering the dynamic headway of successive vehicles, in which the complex characteristic and driving behavior difference between drivers are taken into account. The relationship between the flow rate and the traffic density is obtained by the numerical simulation, and it shows a two-dimensional region in the flow density plane. The three traffic phases, i.e., free flow, synchronized flow, and wide moving jams, are exhibited. It indicates that the synchronized flow and traffic jams can appear even if there is no traffic bottleneck. Besides, the high speed car-following phenomenon is indicated when the traffic is in the synchronized flow. The rate of the high speed car-following is in good agreement with the measured result.

Lane by lane analysis of vehicle time headways — Case study of Izmir ring roads in Turkey

The studies on headway characteristics of traffic flow in Turkey are mainly dealing with signalized arterials, intersection approaches or roundabouts. Only a few studies can be found on headway characteristics of uninterrupted flow facilities and nearly none of them are on lane by lane basis. In this study, the authors are modeling headways of vehicles at zmir ring roads by lane by lane principle. The study demonstrates that lognormal distribution is an effective tool to define headways. In addition, the applicability of Cowan M3 distribution to time headways is investigated. Proportion of free vehicles is modeled for different lanes. The results show that traffic flow in the middle lane is highly affected by the vehicles in other lanes. Vehicles in the outer lane can be assumed to arrive randomly, but they are also disturbed by vehicles in other lanes. These results can be used in capacity and performance analysis of freeway segments, ramp junctions and weaving areas.

Exploring the Pattern of Platoon Dispersion Caused by Traffic Signal

Platoon dispersion is an essential phenomenon to be studied for determining the traffic arrivals at downstream intersections to access the need for signal coordination and to optimize the signal timing plans in coordinated signal systems. However, platoon dispersion is difficult to determine compared with other traffic flow parameters such as traffic volume, saturation flow, and speed. This paper explores the pattern of platoon dispersion caused by traffic signal, in which the focus are given on vehicle headway, intra-platoon headway and inter-platoon headway. Field investigation was made by means of videotapes which record traffic flows at selected location at Senai, Johor. After processing the data, vehicle headway at several consecutive points downstream of traffic signal were obtained and used for the platoon dispersion analysis. The dispersion patterns obtained from the observation during peak and off-peak periods were fitted with the theoretical distribution models. It was observed that vehicle headways follow the Erlang and Shifted Negative Exponential Distribution, intra-platoon headways have Normal Distribution and inter-platoon headways do not fit the tested distribution model.

Time-Headway Distributions on an Expressway: Case of Bangkok

Traffic flow modeling is one of the fundamental keys to solving a traffic engineering problem. Among many parameters, time headway is frequently used to model traffic flow characteristics. A statistical analysis of time headways is immensely important to both theoretical traffic modeling and simulation-based traffic modeling. Basically, it allows researchers to describe an inherently random pattern of traffic flows. Past studies have mainly focused on the time headways of vehicles on highways, freeways, and arterials. However, studies of time headways on urban expressways are rather limited and still need further investigation. In this paper, the author investigates and characterizes the time-headway distributions of vehicles traveling on an urban expressway in Bangkok, Thailand. Particularly, the exponential distribution, the lognormal distribution, and the generalized extreme value (GEV) distribution are used to model the time headways. It is found that the GEV distribution is most effective in modeling time headways. In fact, the GEV distribution can describe more than 90% of the empirical distributions on most lanes and sections of the expressway. On the other hand, the exponential distribution is the least effective distribution. It can only describe the empirical distributions during the periods when the traffic is extremely light.

A Gaussian Kernel-Based Approach for Modeling Vehicle Headway Distributions

Headway distribution models are essential for studying traffic flow theory, roadway accidents, and microscopic traffic simulations. Previous work has focused on parametric models. Vehicle headways were considered to follow some known parametric distributions based on certain assumptions. However, these assumptions are not universally acceptable and, consequently, the reliability of those headway distribution models varies significantly when applied to different flow conditions. In this study, a nonparametric distribution model with Gaussian kernel functions is introduced and assessed for vehicle headways on urban multilane freeways. Without any assumptions, Gaussian kernel models can extract intrinsic patterns from observed headway data to describe the distributing attributes of headways. Experiments were conducted to evaluate the accuracy of Gaussian kernel models for modeling vehicle headways. Results from the experiments indicated that the proposed models outperformed traditional parametric methods in a wide range of flow rates. Furthermore, transferability tests of the nonparametric model were performed, and the results showed that the proposed models can be generalized for applications at other locations with similar traffic flow patterns.

Cellular automata model for traffic flow with safe driving conditions

In this paper, a recently introduced cellular automata (CA) model is used for a statistical analysis of the inner microscopic structure of synchronized traffic flow. The analysis focuses on the formation and dissolution of clusters or platoons of vehicles, as the mechanism that causes the presence of this synchronized traffic state with a high flow. This platoon formation is one of the most interesting phenomena observed in traffic flows and plays an important role both in manual and automated highway systems (AHS). Simulation results, obtained from a single-lane system under periodic boundary conditions indicate that in the density region where the synchronized state is observed, most vehicles travel together in platoons with approximately the same speed and small spatial distances. The examination of velocity variations and individual vehicle gaps shows that the flow corresponding to the synchronized state is stable, safe and highly correlated. Moreover, results indicate that the observed platoon formation in real traffic is reproduced in simulations by the relation between vehicle headway and velocity that is embedded in the dynamics definition of the CA model.

Investigation of Cyclic Vehicle Queue and Delay Relationship for Isolated Signalized Intersections

Delay is the one of the design criteria that has been used for performance evaluation of signalized intersections. In pre-timed control, vehicle delays can be minimized or reduced by proper design of signal timings and phasing. But vehicle delays include many parameters such as signal timing, number of phases, vehicle headways, saturation flow, queueing etc. Among these parameters vehicle queue is formed by unbalanced signal timings or unexpected demand values. On the other hand, headways of vehicles can be effective on queue forming especially in discharging case. It is also related to driver behaviors. In this paper, relationship of cyclic vehicle queue and vehicular delay is investigated considering different signal timings and phase sequencing. The data are obtained from observations that are made at urban intersections of Denizli city, Turkey. Regression analysis is used for relationship and statistical tests are applied. The MuLReD (Multiple Linear Regression Analysis based Delay Estimation) model is developed and significance of the model is proved by statistically. Adjusted R2 value of MuLReD Model is obtained as 0,95. On the other hand, delay values obtained from the MuLReD Model and Akcelik Vehicle Delay Formula are compared with each other. As a result of comparisons, Mean Square Error (MSE) values for Akcelik Equation and the MuLReD Model are determined as about 112 and 7 respectively. These results show that outcomes obtained by the MuLReD Model are closer to field observations. Discussions about the present delay formulas, are also reported in the paper.

Fractal behavior in the headway fluctuation simulated by the NaSch model

The fractal behavior of traffic flow is studied by the adaptive fractal analysis method on the basis of the vehicle headway time series, which are obtained from the numerical simulation of the NaSch model. We find that the vehicle headway time series has a fractal behavior that is similar to the standard Brownian motion (BM) over a wide range of scales when the density is low. As the density increases well-defined sharp spectral peaks, corresponding to the stop-and-go waves, appear while the scale range showing BM-like behavior rapidly shrinks. In the high density regime, a new type of fractal behavior with long-range correlations appears, accompanying the worsening of traffic congestions. The underlying dynamics of traffic flow is analyzed, and some meaningful results are obtained.

Transit Vehicles Intelligent Scheduling Optimization Based on the Division of Characteristic Periods

Vehicle scheduling problem (VSP) is a vital part of the bus scheduling scheme based on the bus timetables. In this paper, the various basic problems that influence the vehicle scheduling scheme are analyzed. Then, the characteristic periods are divided by using the ordered samples clustering of travel time based on vehicle real-time GPS data. According to the parameters such as the vehicle headways, vehicle turnaround time, the first and last stations’ layover time in different characteristic periods, the vehicles scheduling optimization model is established with the object of the minimum vehicles quantity and the minimum total operating costs. The single depot vehicle scheduling problem is converted to the general fixed job scheduling matters; the practical method of vehicle dispatch and vehicle operational method are given. Finally, the model is applied with the actual running data of an example bus line and the corresponding vehicle turnover program is given.

Freeway Travel-Time Estimation Based on Temporal–Spatial Queueing Model

Travel time serves as a fundamental measurement for transportation systems and becomes increasingly important to both drivers and traffic operators. Existing speed interpolation algorithms use the average speed time series collected from upstream and downstream detectors to estimate the travel time of a road link. Such approaches often result in inaccurate estimations or even systematic bias, particularly when the real travel times quickly vary. To get rid of this problem, Coifman proposed a creative interpolation algorithm based on kinetic-wave models. This algorithm reconstructs vehicle trajectories according to the velocities and the headways of vehicles. However, it sometimes gives significant biased estimation, particularly when jams emerge from somewhere between the upstream and downstream detectors. To make an amendment, we design a new algorithm based on the temporal-spatial queueing model to describe the fast travel-time variations using only the speed and headway time series that is measured at upstream and downstream detectors. Numerical studies show that this new interpolation algorithm could better utilize the dynamic traffic flow information that is embedded in the speed/headway time series in some special cases.

Time headway distribution of probe vehicles on single and multiple lane highways

Time headway distribution modeling is fundamental to many aspects of traffic flow studies such as capacity estimation, safety analysis, and microscopic simulation. Existing time headway distribution models have focused on the behavior of general vehicles. We examine the distribution of sampled vehicle headway (e.g., probes) on both single and multiple lane highway traffic streams. This study is divided into three parts: an empirical study, a simulation analysis, and an analytical derivation. The empirical study uses probe data obtained from Houston, Texas that was collected as part of the Automatic Vehicle Identification system of the Houston Transtar system. In the empirical study, a shifted negative exponential distribution was found to give the closest fit for both single and multiple lane cases. We found that if the volume level of the probes is low, regardless of the volume level of general vehicles, the headway of the probes followed a shifted negative exponential distribution. In the simulation study, we found that the time headway of probes does not necessarily follow the time headway distribution of general vehicles. Rather, it depends on many variables such as the volume level of general vehicles, the market penetration of probe vehicles, and the number of lanes. However, when the volume level of general vehicles is low, the headway of probes tends to follow the shifted negative exponential distribution at all levels of market penetration, together with the general vehicles. We analytically proved that if the time headway of general vehicles follows the shifted negative exponential distribution, then the time headway of the probes is the same as that of the general vehicles.

Vehicle-to-vehicle connectivity on two parallel roadways with a general headway distribution

Vehicle-to-Vehicle (V2V) connectivity via a multihop connectivity process underlies vehicular ad hoc networks that enable vehicles to disseminate traffic-related information through short-range wireless communication. In this paper, we propose analytical models for the vehicle connectivity on two parallel roadways, assuming general distributions for vehicle headways. Specifically, we derive models for the expectation, variance and probability distribution of information propagation distance. Closed form approximation to the expectation is developed and is numerically shown to agree well with the exact models. Monte Carlo simulation results further validate the proposed models. Through simulations, the developed models are also shown to have overcome the deficiencies associated with the commonly used one-roadway models and models with the Poisson assumption for vehicle distribution.

Vehicle headway distribution in work zones

This study investigates work zone vehicle headway distribution by disaggregating the vehicle headways into four types: car–car, car–truck, truck–car and truck–truck. It first confirms that the four types of vehicle headways are significantly different by performing the Kruskal–Wallis and Mann–Whitney tests using work zone headway data from Singapore. The statistical test results further show that four factors – traffic flow rate, percentage of trucks, work intensity and lane position – have a significant impact on each type of vehicle headway in work zones. A useful methodology is thus proposed to determine the best-fitted headway distribution model for each type, which includes two procedures: determining the best distribution pattern for each type using the maximum-likelihood estimation and Kolmogorov–Smirnov test techniques and formulating the distribution model parameters as a function of the aforementioned four factors.

Impacts of Snowy Weather Conditions on Expressway Traffic Flow Characteristics

Snowy weather will significantly degrade expressway operations, reduce service levels, and increase driving difficulty. Furthermore, the impact of snow varies in different types of roads, diverse cities, and snow densities due to different driving behavior. Traffic flow parameters are essential to decide what should be appropriate for weather-related traffic management and control strategies. This paper takes Beijing as a case study and analyzes traffic flow data collected by detectors in expressways. By comparing the performance of traffic flow under normal and snowy weather conditions, this paper quantitatively describes the impact of adverse weather on expressway volume and average speeds. Results indicate that average speeds on the Beijing expressway under heavy snow conditions decrease by 10–20 km/h when compared to those under normal weather conditions, the vehicle headway generally increases by 2–4 seconds, and the road capacity drops by about 33%. This paper also develops a specific expressway traffic parameter reduction model which proposes reduction coefficients of expressway volumes and speeds under various snow density conditions in Beijing. The conclusions paper provide effective foundational parameters for urban expressway controls and traffic management under snow conditions.

Estimating the parameters of Cowan’s M3 headway distribution for roundabout capacity analyses

Capacity models based on the gap acceptance theory are widely used in unsignalised intersections and roundabout capacity analysis. These models are based on the statistical distribution of major vehicle headways. In this field, Cowan’s M3 distribution is usually recognized as the most adequate, but the estimation of its parameters is not trivial. In this paper, the main estimation methods are reviewed and a new method (Simultaneous Numerical Estimation – SNE) is proposed. The SNE method was used to develop a calibrated relation between parameters, using field data collected in Portuguese roads and roundabouts. It was determined that the new formula improves capacity estimates, either in one-lane or in two-lane roundabouts. The paper also addresses the importance of each input variable and parameter in the resulting capacity model, through a sensitivity analyses.

A Stochastic Model of Passenger Generalized Time Along a Transit Line

Along a transit line, vehicle traffic and passenger traffic are jointly subject to variability in travel time and vehicle load hence crowding. The paper provides a stochastic model of passenger physical time and generalized time, including waiting on platform and in-vehicle run time from access to egress station. Five sources of variability are addressed: (i) vehicle headway which can vary between the stations provided that each service run maintains its rank throughout the local distributions of headways; (ii) vehicle order in the schedule of operations; (iii) vehicle capacity; (iv) passenger arrival time; (v) passenger sensitivity to quality of service. The perspective of the operator, which pertains to vehicle runs, is distinguished from the user's one at the disaggregate level of the individual trip, as in survival theory. Analytical properties are established that link the distributions of vehicle headways, vehicle run times, passenger wait times, passenger travel times, and their counterparts in generalized time, in terms of distribution functions, mean, variance and covariance. Many of them stem from Gaussian and log-normal approximations.

Analysis on Capacity and LOS of Signalized Intersection in Slight Snowy Weather

In order to evaluate traffic operation status of signalized intersection in slight snowy weather, it was studied that the influence of slight snow on capacity and level of service (LOS) of signalized intersection. Data of timing, vehicle’s starting loss time, saturated headway of straight vehicle and delay of one signalized intersections in sunny and slight snowy weather were observed by video method. Stop-line method was adopted to calculate the capacity of signalized intersection and LOS was evaluated by American standard. It showed that capacity of signalized intersection decreased by 7% and LOS declined one level in slight snowy weather compared with on sunny weather.