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The uneven distribution of travel demand is incredibly commonplace in cities, but insufficient attention has been paid to this problem. In this paper, we explore the impact of the uneven distribution of travel demand on an accessible network. A model with a sparse and dense mixed grid transit network based on an uneven distribution of travel demand is proposed to provide a high-performance bus service. The transit network was composed of two parts: a dense grid network in the downtown area and a sparse grid network in the periphery. The objective function of the model included agency cost and passenger cost, where the decision variables were the downtown-to-city ratio, the downtown headway, stop spacing (line spacing), and ratio of the periphery headway to the downtown headway. This study validated the proposed model using the demands of San Francisco. The concentrated spatial demand resulted in a lower total cost, whereas the varying travel demand must be controlled within an appropriate range to maintain the bus performance. The stable bus lines and stops with a variable timetable of the proposed model are profitable for fast-growing cities.

Headway is a safety measure commonly used to investigate driving behaviour and driver performance. Its purpose is to reflect the following distance or time between a leading and following vehicle in traffic. It is therefore associated with drivers’ response time, such as in braking or swerving, during safety critical events. In the literature, distance and time headway are defined in different ways, despite standard definitions in the traffic engineering literature, which prompted this systematic review of headway definitions across a range of study designs, in order to recommend approaches to improve the accuracy and reproducibility of headway definitions used in road safety contexts. PRISMA guidelines were followed to search four databases (EMBASE, COMPENDEX, SCOPUS and MEDLINE) for studies that reported on headways or discussed methodological approaches. The search and filtering of abstracts identified 110 articles for a qualitative synthesis. Four broad approaches to measuring headways were detected: studies using simulation, roadside external features, on-road features, and on-vehicle features. Studies were coded as to whether they included written explanation, mathematical statements, or pictorial depictions of headway. Only 49.6% of studies contextualised headway sufficiently for reproducibility. Reproducibility is crucial for accurate interpretation of research findings and comparisons across studies. It is recommended that headway definitions should a) exclude vehicle or parts of vehicle lengths, b) include reference points (e.g., bumper/axle/rear), c) have a consistent terminology, and d) include the accuracy of headway measuring devices to report the precision of a study’s findings.

Purpose To evacuate passengers arriving at intercity railway stations efficiently, metros and intercity railways usually share the same station or have stations close to each other. When intercity trains arrive intensively, a great number of passengers will burst into the metro station connecting with the intercity railway station within a short period, while the number of passengers will decrease substantially when intercity trains arrive sparsely. The metro timetables with regular headway currently adopted in real-world operations cannot handle the injected passenger demand properly. Timetable optimization of metro lines connecting with intercity railway stations is essential to improve service quality. Design/methodology/approach Based on arrival times of intercity trains and the entire process for passengers transferring from railway to metro, this paper develops a mathematical model to characterize the time-varying demand of passengers arriving at the platform of a metro station connecting with an intercity railway station. Provided the time-varying passenger demand and capacity of metro trains, a timetable model to optimize train departure time of a bi-direction metro line where an intermediate station connects with an intercity railway station is proposed. The objective is to minimize waiting time of passengers at the connecting station. The proposed timetable model is solved by an adaptive large neighborhood search algorithm. Findings Real-world case studies show that the prediction accuracy of the proposed model on passenger demand at the connecting station is higher than 90%, and the timetable model can reduce waiting time of passengers at the connecting station by 28.47% which is increased by 5% approximately than the calculation results of the generic algorithm. Originality/value This paper puts forward a model to predict the number of passengers arriving at the platform of connection stations via analyzing the entire process for passengers transferring from intercity trains to metros. Also, a timetable optimization model aiming at minimizing passenger waiting time of a metro line where an intermediate station is connected to an intercity railway station is proposed.

The modeling complexity of the traction power system and variation of traffic conditions bring challenges for the optimization of energy management strategy for supercapacitor energy storage systems in urban rail transit. Therefore, in this paper a deep-reinforcement-learning-based energy management strategy is proposed: the energy management system is modeled as an intelligent agent, the reward function is formulated comprehensively considering the energy-saving and voltage-stabilizing effects of supercapacitor, a traction power system simulator is developed to emulate the environment, and the agent’s behavior is improved in each headway through the deep Q-learning algorithm, and converges to the nearly-optimal policy. The proposed strategy is verified through simulation based on the Beijing Subway Batong Line. The study results show that it dynamically adjusts the voltage thresholds so as to better allocate the supercapacitor capacity along the time horizon. The energy-saving and voltage-stabilizing effects are significantly improved compared with the fixed-threshold strategy and genetic optimization, and demonstrating to be in close proximity to the optimal benchmark deduced from dynamic programming.

Some stops on busy bus lines regularly suffer from bus bunching, which refers to a bus arriving with a little headway to its predecessor. This phenomenon increases scheduling difficulties and has a negative impact on the passenger experience due to unreasonable scheduling. The conventional holding strategy aims to alleviate this problem by holding buses at control points. However, the holding strategy has the drawbacks of creating large deviations from the original schedule and prolonging passenger waiting time when confronted with traffic congestion. This study proposes an innovative short-turning strategy to alleviate bus bunching by the deliberate conversion of a few regular trips to short-turning trips. A nonlinear optimisation model is developed by rescheduling a set of trips using the short-turning strategy to minimise schedule deviation from the original schedule. The nonlinear short-turning model is then converted into a linear form that is solvable by CPLEX. Based on real data from the Yuntong 111 bus line in Beijing, China, the proposed short-turning strategy is deployed in a simulation experiment. The results show that the short-turning strategy is superior at alleviating bus bunching than the alternatives of no control strategy and the holding strategy. Compared with no control strategy, the short-turning strategy can achieve a more than 43.44% reduction in schedule deviation and significantly reduce total passenger waiting time by up to 8.99%.

Driving simulator (DS) can be introduced as a popular tool in the transport industry for the analysis of driving behavior. Simulators should be validated in term of each specific recorded data, separately. When DSs are the source of data, each part of data which indicates a maneuver or a behavior should be validated separately. This paper is dedicated to the validation of car-following distance in the presence of considerable traffic flow which may increase mental load. In general, the results showed that DSs could be used for determining the behaviors of a driver by focusing on headway and car-following behavior. The distance kept by male drivers was shorter than female participants. It was seen that drivers between the ages of 20–40 were suitable for car-following tests in the DS by considering all real driving tasks in a congested traffic condition. Car-following threshold headway was less than 2 s for 85% of the participants, which is quite similar to the past results from real data in Iran. The average car-following headway threshold in the DS was 0.8 s, which is less than field data (1.2 s).

Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping.

In the earlier decade, Smartphones have been made and dynamically fused with people’s lives for social use, yet master uses too. The headway of correspondence developments impacts influences the wa...

In the following process, in order to improve the driving safety and road utilization of the adaptive cruise control (ACC) system, a variable time headway spacing strategy was studied. In view of the fact that the variable spacing strategy cannot adapt to the complex and variable deceleration conditions, an improved variable time headway strategy is proposed, which changes with the deceleration time and deceleration of the preceding vehicle. Based on this, the upper controller of adaptive cruise control based on model predictive control is designed, and numerical simulation of the variable time headway spacing strategy is performed, which verifies the effectiveness of the improved variable time headway strategy. The results show that the spacing strategy proposed in this paper can more smoothly keep up with the preceding vehicle, and improve driving safety, comfort and road utilization.

In a parameter study, we systematically varied parameter values, and quantified the resulting traffic flow in each individual lane. We modeled two-, three-, and four-lane freeway sections with the microscopic traffic flow simulation tool PTV Vissim. We compared the results with findings from literature. Simulations using car following model Wiedemann 99 fit better to empirical studies than those using Wiedemann 74. Empirically determinable parameters, that have a relevant influence on lane flow distribution are desired speed distributions (mean for heavy-duty vehicles and standard deviation for cars), heavy-duty vehicle share, and the gradient of the section. Additionally, the driving behavior parameters CC1 (headway time), CC3 (threshold for entering following), and safety distance reduction factor have an influence. As CC1 is one of the most relevant parameters for calibrating capacity, CC3 and the safety distance reduction factor remain for lane flow adjustment.

We propose an optimal actuation of operational commands for regularizing bus headways. Assuming that a transit headway control system manages the bus operation by issuing reference arrival times at the next station for a bus, the problem is how to implement the control decisions for the bus in terms of three nonexclusive alternative actions: holding the bus at stops; varying bus speeds; and controlling traffic lights. Mathematical programming provides the basis of the approach. The constraints specify the bus trajectory model and the operator objectives are formulated as a multi-objective cost function solved by a lexicographic method. Results for a bus run on a single segment between two stops highlight the properties of the solution. A simulation study of an entire transit corridor in Quebec City, Canada, shows the advantage of the method over both headway control based on pure holding at stops and on one that combines holding and absolute transit signal priority.

The design and management of infrastructure is a significant challenge for traffic engineers and planners. Accurate traffic characterization is necessary for effective infrastructure utilization. Thus, models are required that can characterize a variety of conditions and can be employed for homogeneous, heterogeneous, equilibrium and non-equilibrium traffic. The Lighthill-Whitham-Richards (LWR) model is widely used because of its simplicity. This model characterizes traffic behavior with small changes over a long idealized road and so is inadequate for typical traffic conditions. The extended LWR model considers driver types based on velocity to characterize traffic behavior in non lane discipline traffic but it ignores the stimuli for changes in velocity. In this paper, an improved model is presented which is based on driver reaction to forward traffic stimuli. This reaction occurs over the forward distance headway during which traffic aligns to the current conditions. The performance of the proposed, LWR and extended LWR models is evaluated using the first order upwind scheme (FOUS). The numerical stability of this scheme is guaranteed by employing the Courant, Friedrich and Lewy (CFL) condition. Results are presented which show that the proposed model can characterize both small and large changes in traffic more realistically. Doi: 10.28991/cej-2021-03091632 Full Text: PDF

In the upcoming decades, connected vehicles will join regular vehicles on the road, and the characteristics of traffic flows will change accordingly. To better understand mixed traffic flow (regular vehicles and connected vehicles) characteristics, a generic car-following modeling framework for this new mixed traffic flow on an urban road under a connected vehicle environment is proposed in this paper. Considering a vehicle’s speed, which is affected by the speed of the preceding vehicle, an improved intelligent driver model (IDM) is used as the car-following model for regular vehicles. An extended cooperative adaptive cruise control (CACC) based on the nonlinear dynamic headway strategy was established as the car-following model for connected vehicles. The fundamental diagram model of mixed traffic flow under different market penetration rates of CACC vehicles is investigated, and the traffic flow operation mechanism of connected vehicles is analyzed to improve the capacity. In addition, simulation experiments on urban roads are designed to evaluate the queue time and length of vehicles passing through congested sections under different market penetration rates of CACC vehicles. The results demonstrate that the proposed model can effectively describe the current situation of traffic flow on urban roads under different market penetration rates of CACC vehicles. The increase in the market penetration rates of CACC vehicles can significantly improve the traffic flow efficiency. When CACC vehicles reach 100% of all vehicles, the queue length on congested roads can be shortened by 64.6%, and the total travel time on congested roads can be reduced by 48.3%.

Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply - demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique. On the demand side, the dynamic passenger traffic demand with agents is modelled from perspective of boundedly rational travel decision. On the supply side, the dynamic service supply of train traffic with agent is modelled. The headway time is designated as the main decision variable, for the key link between the passenger demand and service supply is the headway time in different time-of-day intervals. To make the passenger demand more closely matched with service supply in urban rail transit network system at the reasonable travel cost and operational cost, the calculation formula for matching degree is proposed, along with the distributed system architecture for agent-based matching mechanism, and the negotiation-based iterative mechanisms for balancing. The proposed methods are validated on the simulation platform NetLogo. The simulation results emphasize the importance of representing the supply side and the demand side jointly/interactively. These findings are meaningful for policies on both development of efficient capacity usage strategies of urban rail transit network and provision of high level of service for passengers.

Drivers' phone use during the red traffic signal and queue discharge activity were investigated at five signalized intersections in Hangzhou, China, with the aims to explore factors influencing phone use behavior while drivers are waiting for a red traffic signal and the impact on the queue discharge. Observations were conducted by recording videos at each intersection during different times of the day and on different days of the week. Of the 2596 drivers observed while waiting at red traffic signals, 36.7% used their phone for some purpose and 20.8% used it until-green. It was found that phone use was not even restricted to within the red-phase of the traffic signal. About 7% continued the use of their phone beyond the redphase while moving off. Logistic regression analyses with phone use as the dependent variable showed that red countdown timer, waiting time, the fact whether the driver was on its own or with a passenger, and the type of vehicle were significant covariates. Start-up delays for queues, with queue leaders whether using their phone or not, were compared using t-test of difference between means. The driving performance, with reference to vehicle headway, was also analyzed against driver phone use. Results reveal that while phone use that was ceased before the change to green has an insignificant effect on the start-up delay, until-green phone use, especially phone use that was continued after-red while moving off, significantly affected the start-up delay and driving performance negatively.

Although converting an intersection into a roundabout has been shown to result in fewer injury accidents for both motor vehicle drivers and pedestrians, the effect on bicyclists’ safety is unclear or even negative. This study focuses on roundabouts without bicycle facilities (i.e., mixed traffic conditions) and makes use of semi-automated video observation software with the aim of analysing bicyclists’ behaviour and safety on roundabouts with different diameter. Four urban roundabouts in Belgium are observed. Interactions between bicyclists and other vehicles are analysed using speed, lateral position and five indicators to describe the closeness of interactions (TTCmin, PET, T2 min, lateral overtaking proximity and minimum distance headway). Additionally, the lateral position and riding speed of bicyclists that are in interaction with other vehicles is compared with the behaviour of bicyclists that are not in interaction with other vehicles. The behavioural analysis revealed that regardless of the type of condition (free-flow bicyclists or different interactions bicyclist-car), bicyclists always ride faster on roundabouts with big diameter and slower on roundabouts with small diameter. Moreover, bicyclists ride closer to the central island on roundabouts with big diameter compared to roundabouts with small diameter for all the conditions analysed. The analysis of surrogate safety indicators (TTCmin, PET, T2 min) revealed that close interactions between bicyclists and cars are relatively frequent at both small and big roundabouts. The percentages of close interactions are more or less equal for roundabouts with big diameter (7.86% of observed interactions) and roundabouts with small diameter (8.24%). The analysis of the indicators to describe the closeness of interactions also showed that the closest interactions at roundabouts are all situations where the bicyclist has a leading role. The analysis of the most common types of close interactions revealed indeed that the most common close interactions are interactions where the bicyclist is entering the roundabout. The analysis of lateral overtaking proximity showed that bicyclists who overtake a car take smaller lateral overtaking proximities compared to cars overtaking a bicyclist. The analysis of minimum distance headway finally revealed that bicyclists who ride behind a car take smaller distance headways compared to cars driving behind a bicyclist.

This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for fleet control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for fleet control in the case of CAVs’ homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.

This paper explores new methods for regression models to understand the evolution of headway irregularity of public transport services. In particular, this article is structured around an urban bus...

This paper proposed a deep reinforcement learning based energy management strategy in the connected traffic environment. At the upper layer, a deep deterministic policy gradient algorithm is used to systematically integrate ego vehicle reference speed planning into the energy management strategy in the car following scenarios. At the bottom layer, a driver model and an adaptive equivalent consumption minimization strategy are implemented to conduct the optimal power split control based on the planned reference speed. As distance headway, fuel consumption, and terrain information are taken into consideration by the deep reinforcement learning controller, the proposed strategy can not only maintain a safe distance headway but also improve the energy efficiency of the entire driving cycle. Finally, the fuel consumption score of the proposed strategy is reduced by 3.5% compared with a car following strategy based on a proportional-integral controller. The simulation results show that the proposed strategy can learn a reasonable car following policy through the training process. In addition, the simulation duration for each reference speed planning step is only about 4.1 ms, which proves the proposed strategy can be applied online.

Entry capacity is an important parameter for evaluating the roundabout performance. In general, entry capacity is influenced by three gap parameters: critical gap, follow-up time and minimum headway of circulating vehicles. These gap parameters certainly are correlated with the driving behavior as well as the roundabout characteristics. In this paper, video records of two single roundabouts in the state of Qatar were used to estimate the gap parameters. In addition, the entry capacity of both roundabouts was estimated and compared through different methodologies. Results showed that the entry capacity is directly related with the inscribed circle diameter (ICD) until 400 vph of circulating flow. However, it is inversely related with ICD if the circulating flow exceeds 400 vph. Moreover, we found that the entry capacity has a negative relation with the gap parameters. Estimated entry capacities with the calibrated Highway Capacity Manual 6th Edition model (HCM, 2016) were significantly higher than estimated ones through the proposed approach by Qatar Highway Design Manual (QHDM, 2015). Interestingly, the entry capacity was underestimated in both approaches if default gap parameter values are used compared to the calibrated models using the observed gap parameters (36.7% less for HCM, and 19.4% less for QHDM). Thus, the utilization of realistic gap parameter values representing local traffic condition is essential for traffic planners to accurately estimate the entry capacity and accordingly ensure feasible design of different types of roundabouts.

In major metropolitan cities, metro and intercity rail services usually share stations or have stations in close proximity to facilitate smooth connections for passengers. A large number of passengers may arrive on metro platforms within a short period of time upon the arrivals of intercity trains, while the number may drop significantly afterward. This injected passenger demand is not coped well by the metro timetable with the commonly adopted regular train headway. Focusing on metro lines connecting to intercity railways, this study puts forward a timetable model to optimize train headways at each station, assuming all passengers board the first train available. The objective is to minimize the total waiting time of passengers at platforms throughout the metro line, without changing the number of vehicle trips. To calculate the passenger waiting time accurately, a mathematical model is proposed to obtain the number of transfer passengers arriving at metro platform in each time interval, based on arrivals of intercity trains and transfer facilities at the station. A genetic algorithm, combined with interior-point algorithm, is developed to obtain the solution of the proposed timetable model. Real-life case studies show that the optimized timetable reduces the total passenger waiting time when compared with the current timetable and the solution of another model optimizing train departure times at the first station only. In addition, the peak number of passengers congregating at metro platforms drops significantly, which relieves passenger crowding on the platform.

The development of green structures must begin with the decision and use of eco-obliging materials with related or favored features over standard structure materials. Building materials are commonly picked through handy, particular and budgetary necessities. In any case, with acceptability as a fundamental issue in the latest decades, the structure part, genuinely or in an indirect way causing a great piece of the yearly environmental debilitating, can take up the promise to add to viable progression by finding even more earth big-hearted methods for advancement and building. Among the orientation for courses of action is to be found in new material applications, reusing and reuse, acceptable formation of things or use of green resources, Careful assurance of eco-pleasing attainable structure materials may be the speediest way for engineers to start joining down to earth plan thoughts in structures. Generally, cost has been the fundamental idea when taking a gander at related materials or materials decided for near explanation. Before long, the expense of a structure part infers just the gathering and transportation costs, not social or environmental costs. Impressive exercises have been finished by the assessment arrange comprehensive, in order to discover elective sensible structure materials and low advancement strategies, which realize an undeniably acceptable and moderate improvement consenting to the comfort standards required today. Getting a handle on green structure materials is a respectable decision to meet to this objective. Thus, Selection of improvement materials that have least environmental loads is significant in the acceptable headway of a nation. The inspiration driving this paper is to highlight how reasonable structure material can add to lessen the impact of biological debasement, and make sound structures which can be affordable to the inhabitant similarly as our condition.

This paper investigates the robust distributed cruise control problem of multiple high-speed trains under external disturbances. First, by modeling each train as a cascade of point masses connected by spring-like couplers, the longitudinal interaction between adjacent cars are represented by the connected topological graph. Then, under the framework of the communication-based train control technology, the interaction of desirable speed information among trains and the wayside control center is described by the directed topological graph. Next, a distributed cruise controller is designed by taking advantages of the graphic theory such that the multiple trains track different target speeds, and both the distance of neighboring cars and the headway of successive trains are kept in appropriate ranges. Finally, to eliminate the influence of external disturbances, we adopt the disturbance observer to approximate the perturbations, and present a sufficient condition for the existence of the distributed control strategy and the observer gain parameter in form of the linear matrix inequality (LMI). Numerical experiments illustrate that the composite control law is effective in inhibiting the external disturbances, and guaranteeing the safety, efficiency and comfort of high-speed trains’ movement.

This article explains the challenges faced by women employees in the corporate world and how a focused development programme can help organisations foster female talent and make significant progress towards creating a gender inclusive culture. In today’s complex and dynamic age, where technology, processes and business models change rapidly, women struggle to balance their career aspirations with their personal challenges, societal pressures and their own deep-set inhibitions. This leads to a widening gender gap from entry-level positions to senior leadership roles. The situation, therefore, necessitates a seismic shift in the thought process of female employees to help them overcome internal and external challenges, fast-track capability improvement and make deep behavioural impacts to foster career growth. This article highlights the need for a structured development programme to help organisations invest in their women employees in mid-management roles and create a pathway for them to achieve their career goals. All actions are documented in the programme to culminate into actionable insights for participants and deep cognitive insights for L&D and organisations.

The various performances of buses at stations bring lots of difficulties for operators to manage them to improve the service quality. This paper proposes a data-driven framework to analyze the patterns of stations with network structure data, points of interest (POI) data and vehicle global positioning system (GPS) trajectory data. First, we build six indicators based on these data to measure the performance from station perspective. The results show that the number of POI around stations within 1 kilometer follows an exponential distribution. Moreover, the average headway and headway deviation of stations follow lognormal distributions. Second, we use agglomerative hierarchical clustering method to divided bus stations into different groups. Results indicate that the bus stations of Jinan could be divided into four groups with obvious characteristics. The findings could help operators to make exclusive strategies to manage bus systems.

Modeling the vehicle headway data is fundamental for intelligent transportation applications in traffic engineering. It is useful for the traffic signal optimization and flow modelling. Exponentiated Weibull (EW) is one of the best flexible model for characterizing uncertainty in various fields of data. In this study, we revisit EW distribution and propose to use of ranked set sampling as a useful sampling method for estimating the unknown parameters. We deal with the performance of ranked set sampling and simple random sampling methods by a simulation study in R-software in terms of mean squared errors. We estimate the parameters of EW distribution using the maximum likelihood method under the assumption that all parameters are unknown. We illustrate the flexibility and the usefulness of EW distribution by analysing generated data from a real application study in transportation field.

Bus scheduling is a well-known NP-hard problem, and it is addressed with the use of heuristic solution methods or graphical approaches. In this study, the author proposes an improved formulation of the bus scheduling problem that considers the vehicle availability, the vehicle capacity and the allowed headway variability among successive trip dispatches. His formulation expands the classic bus scheduling model formulation by including the aforementioned features. In his study, the bus scheduling problem is understood as the problem of setting the optimal dispatching times for a set of pre-determined daily trips of a particular bus line. His model facilitates the search of solutions that can improve the waiting times of passengers while meeting the operational requirements and avoiding overcrowding. His proposed mathematical program is proved to be non-convex, and it is solved with heuristic solution methods because numerical optimisation approaches cannot guarantee a globally optimal solution. The performance of his approach is tested in a case study using real operational data from bus line 302 in Singapore. A simulation-based evaluation demonstrates potential gains of up to 20% on average passenger waiting times and a major reduction in refused passenger boardings because of overcrowding.

This paper presents an optimization algorithm for energy-efficient driving of electric vehicles. The algorithm is based on Pontryagin's Minimum Principle by considering the driving mission as an optimal control problem. On a planned route, diverse application cases may occur due to various physical traits such as powertrain (e.g., switching between motor and generator operation), environmental variations (e.g., terrain grade change), traffic laws (e.g., speed limits), and safety concerns (e.g., headway to the leading vehicle). The proposed algorithm handles these real-world challenges by considering the perturbation to the system dynamics and the constraints to the control and state variables.

This paper attempts to disclose the features of the mixed traffic flow of manually driven vehicles (MVs) and autonomous vehicles (AVs). Considering dynamic headway, the mixed traffic flow was modelled based on the improved single-land cellular automata (CA) traffic flow model (DHD) proposed by Zhang Ningxi. The established CA model was adopted to obtain the maximum flow of the mixed traffic flow and was analyzed under different proportions of AVs. On this basis, the features of the mixed traffic flow were summarized. The main results are as follows: the proportion of AVs has a significant impact on the mixed traffic flow; when the proportion reached 0.6, the flow of the whole lane was twice that of the MV traffic flow. At a low density, the AV proportion has an obvious influence on mixed traffic flow. At a high density, the mixed traffic flow changed very little, as the AV proportion increased from 0 to 5. The reason is that the flow of the whole lane is constrained by the fact that MVs cannot move faster. However, when the AV proportion reached 0.8, the flow of the whole lane became three times that at the proportion of 0.6. At the speed of 126 km/h, the flow rate was 2.5 times the speed limit of 54 km/h. The findings lay a theoretical basis for the modelling of multilane mixed traffic flow.

Advanced Driver Assistance Systems in heavy commercial road vehicles (HCRVs) have become necessary with the increasing contribution of HCRVs to road accident fatalities, where human factors form the major contributor. Collision avoidance systems can help in reducing fatalities through automated braking when required. This study focused on the development of a framework for the computation of the minimum time headway, for a Vehicle-to-Vehicle (V2V) communication-based collision avoidance system for HCRVs, that must be maintained through automated braking to avoid a collision. The framework, as well as the developed Collision Avoidance Algorithm (CAA), considers critical attributes of an HCRV such as significant load variations and brake actuator dynamics. Further, the time headway formulation varies with the initial longitudinal speed and considers communication latency. Experiments were conducted in a Hardware-in-Loop test setup to evaluate the efficacy of the proposed formulations. It was observed that the incorporation of communication latency and initial longitudinal speed explicitly in the time headway formulation maintained the desired final spacing between the vehicles, over a range of communication latencies and various host vehicle longitudinal speeds, which was not the case when the above factors were not considered.

The problem of optimizing the operation of Bus Rapid Transit (BRT) lines that share a common corridor is analyzed. The goal is to minimize passengers travel time, including waiting at stations. In the common corridor, stations are shared by several bus lines which present riding alternatives for passengers with origins and destinations within the corridor. Since buses from different lines interact in the corridor, optimal multi-line headways that consider headways across all lines can alter single-line headways so as to maximize user benefit. We develop the system model in a mathematical programming fashion, embedded with the control strategy as bus holding decision variables. The method, named Multi-line Integrated Holding Control Strategy (M-IHCS), implements the required bus headway regulation for multiple lines in an integrated and simultaneous manner. Numerical tests of the optimization approach developed for the system model show the distinctive features of the solutions, indicating the superiority of multi-line headway control over its single-line counterpart.

Despite numerous theoretical models, only limited field experiments have been conducted to investigate traffic oscillation propagation, and the relationships between traffic oscillation features (e.g., period, speed variation, spacing and headway) have not received quantitative analysis. This study conducts a set of field experiments designed to inspect such relationships. In these experiments, 12 vehicles equipped with high-resolution global positioning system (GPS) devices following one another on public roads, and the lead vehicle was asked to move with designed trajectory profiles incorporating various parameters. Measurements of five features are extracted from processing the field vehicle trajectory data with a time-domain method. Frequency analysis is also proposed with the Fourier transform method to verify the effectiveness of the features measured by the time-domain method. Compared to the frequency-domain method, the time-domain method yields more measurements with comparable quality and is more robust on trajectories with a small number of oscillation cycles. Then, a series of linear regression analyses reveal a number of new findings on the relationships between these features. For example, the time gap between two consecutive vehicles is negatively correlated with the speed standard deviation of the preceding vehicle and the initial speed of the following vehicle. It is also positively correlated with the average speed of the preceding vehicle and the initial spacing. The findings are helpful in constructing new microscopic traffic models better describing traffic oscillation dynamics. To illustrate this benefit, revised car following models are proposed to capture the relationship between time gap and other features. The simulation results show that the revised models yield better prediction accuracy (in range of 18% to 40% with the oscillation experiment dataset and in range of 30–63% with the stationary experiment dataset) than the classical models on reproducing real-world trajectories.

Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven bus timetable to substitute the existing experience-based bus timetable, which is now widely used by bus lines. Driven by the bus GPS data and IC card data, a timetable optimization model with time-dependent passenger demand and travel time among stops is proposed. The objective of maximizing passenger volume is based on a new preference-based passenger selection model. The working hours constraint is initially formulated, and the headway constraint and departure time constraints are also taken into account. For handling the step functions in both objective and constraints, we introduce a set of 0–1 variables to transform the proposed model into an integer linear programming. A model contraction approach is provided for solving the medium-scale problems and a two-stage solution method is proposed for the large-scale problems. The proposed model and methodology are tested on a real-world bus line in Beijing. The results show that it is able to produce a satisfactory timetable that outperforms the previously used experience-based one in terms of raising the average passenger volume by 8.2%.

Unexpected service disruptions in transportation systems caused by accidents, system breakdowns, poor weather conditions, etc., are quite common. When disruptions occur, rescheduling of vehicles is often needed in order to mitigate the damage caused by the disruptions. In integrated production and outbound distribution systems, a disruption in outbound distribution operation affects not only the delivery plan but also the production schedule. In this paper, we consider a simple integrated scheduling model of production and outbound deliveries with a minimum headway constraint between vehicle departures, and study the situation where an optimal solution of the integrated scheduling model has been obtained but the delivery service is suddenly unavailable for a certain time period due to some unexpected incidents. We would like to determine a new production and delivery schedule in which no delivery takes place during the unavailable period. The objective is to simultaneously maintain a low cost schedule and control the magnitude of changes in the delivery times of the finished goods. We consider three different ways to control the time disruption, and develop polynomial-time algorithms for the corresponding problems.

Vehicle heterogeneity, lack of lane discipline, lack of infrastructure facilities, and weak enforcement of traffic rules are all characteristics of Indian traffic. These characteristics make Indian roads difficult for non-motorized road users, especially pedestrians. Most intersections in urban areas in India are unsignalized, which results in complex interactions between pedestrians and motorists. This study aims to understand pedestrian gap acceptance behavior at unsignalized intersections in India under heterogeneous traffic conditions. Pedestrian data were collected from six unsignalized intersections in the city of Kanpur. The critical gap was estimated using Wu’s (2006) macroscopic model, and then the effects of several variables on the critical gap were studied. A discrete choice model (binary logit model) was developed to understand the choice of accepted and rejected vehicular gaps depending on traffic, road, and peer pedestrian characteristics. The mean critical headway was 3.76 s using Wu’s model. The behavioral analysis revealed that pedestrians accept rolling gaps to cross unsignalized intersections, resulting in a reduction of the size of the critical gap. The adoption of rolling gap behavior can be attributed to the unyielding behavior of motorists, which forces pedestrians to accept short vehicular gaps in the traffic stream.

Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

Virtual coupling is a promising innovation aimed at increasing railway capacity. Compared to current railway signaling systems, it allows two or more trains to run with reduced headway between them. However, such reduced headways are a challenge to safety. In this work we consider this challenge by formally describing and verifying an approach to virtual coupling. We propose a general modeling method based on topological manifolds to describe the protection logic for virtual coupling train control systems. We also describe the basic train control elements in topological terms and analyze the line condition of our virtual coupling logic. We establish that the line condition safety requirements and its representation as a manifold are equivalent and further provide a formal definition of the concept of a movement authority with manifold notations. This allows us to consider the dynamic behavior of trains and a series of theorems that establish the correctness of our protection logic for virtual coupling. Finally, we apply the presented methods to a case study. The results show that the proposed method provides a suitable way to realize a virtual coupling logic safely.

Rear-end crashes are a major part of road injury burden, accounting for one-third of all vehicle-to-vehicle crashes in New South Wales, Australia. Close following or driving with short headways is a key cause, yet the role of driver behaviour in rear-end crash risk is not well researched. The primary aim of this research was to develop a better understanding of rear-end crashes by assessing headways on Australian roads and investigating driver behaviour and performance associated with close following in crash and non-crash scenarios. Two systematic reviews of headway were conducted. First, a review of research on headway identified the need for a consistent and accurate definition of headway, so the thesis puts forward an improved definition. The second review identified the range of external factors that increase the risk of short headway and increase crash risk including speed, task engagement, lead vehicle type, traffic conditions, road characteristics, weather/visibility, drug use, driving fatigue, innovative lane markings, and various warning systems. These factors were then explored in New South Wales data on rear-end casualty and multiple vehicle crashes. The modelling of these associated factors were confirmed as contributing factors in rear-end crashes, congruent with the review of headway. Higher speed, free flowing traffic, volitional task engagement, low cue environments, and collision warning lead to longer headway. Despite lower fatalities, higher odds of injury were observed for rear-end crashes than other crash types. Rear-end crashes were more likely to lead to multiple vehicle crashes, which had a higher chance of fatality than other types of crashes. Finally, naturalistic driving study data was used to investigate headway during normal driving, exploring close following at different speeds and classifying potential risky driving at various headways. In 64 hrs accumulated across 2101 trips, short headways of under 1 s occurred in around 15% of driving. Common manoeuvres to avoid rear-end crashes when close following were changing lanes, or braking, almost always by the following driver. Headway was associated with both driver speed and posted speed limits, decreasing as posted speed limits increased. Over-the-speed-limit driving was observed in all headway scenarios, but especially in higher speed zones. The findings challenge the notion that rear-end crashes are less severe with low injuries. Road users should be made aware of how frequently safe headways are violated and severity of injury outcomes. Driver education, community engagement, application of driver assistance technology consistent with driver behaviour and safety campaigns need to focus on safer speed and headway management to reduce rear-end crash risk.