Time Headway Research Articles

ANALISIS KUALITAS LAYANAN ANGKUTAN UMUM PENUMPANG (MINIBUS) KOTA DENPASAR BERDASARKAN PERSEPSI MASYARAKAT DENGAN METODE CONFIRMATORY FACTOR ANALYSIS

Public transportation in the city of Denpasar reached its peak in the 1990s with the latest fleet being minibus-type public passenger cars with a capacity of 8-10 people. Along with the pace of motorization and ease of buying cars and motorbikes, there can be an increase in private vehicle ownership in the community. The interest of the people in Denpasar City's use of public transportation has decreased significantly. The community can finally feel and have perceptions about the difference between the advantages of using private vehicles and the quality of public transportation services. The purpose of this study was to explore public perceptions of the quality of public transport services, by distributing 400 questionnaires in all sub-districts of Denpasar City. Data on respondents' perceptions were analyzed using structural equation modeling (SEM). Based on the results of the analysis, there are seven indicators that shape people's perceptions of the quality of public transport, namely: small fleet availability, long waiting and travel times, less convenience, irregular time headway, low frequency of arrivals, and accessibility. The reduced availability of public transport is caused by long waiting times and low frequency of arrivals. Low accessibility due to long travel time. Thus, the quality of services like this in the community makes the use of public transportation in the city of Denpasar decreased.

Stability Analysis of the Vehicular Platoon with Sensing Delay and Communication Delay: CTCR Paradigm via Dixon Resultant

For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the internal stability analysis, the necessary and sufficient internal stability condition is obtained and the exact time delay margins (ETDMs) are derived via the cluster treatment of characteristic root (CTCR) paradigm. A Dixon resultant matrix–based method is proposed to determine the kernel and offspring hypersurfaces of the CTCR paradigm, and then the computational burden of deriving the ETDMs is reduced significantly. In the string stability analysis, we first propose the string stability conditions for the situation no matter how large the frequency of the leader vehicle’s maneuver is. Furthermore, the more practical string stability conditions are studied by considering only the region of low frequency, where most of the energy of the spacing errors exists. Then, a lower bound of the time headway is deduced to enhance road capacity, so the potential of the vehicular platoon is fully motivated. Numerical simulations are provided to illustrate the effectiveness of the theoretical claims.

Integral Sliding Mode Control-Based Robust Bidirectional Platoon Control of Vehicles With the Unknown Acceleration and Mismatched Disturbance

This paper proposes integral sliding mode control (ISMC)-based robust bidirectional platoon control methods in the longitudinal platoon for vehicles with unknown acceleration and mismatched disturbance. A finite time robust estimator (FTRE) is presented to estimate the practically unknown acceleration and mismatched disturbance of adjacent vehicles. In addition, the matched disturbance in the vehicle is also compensated for to further ensure the robustness of the platoon control. A modified constant time headway (MCTH) policy that removes the influence of the initial spacing and velocity error is defined to improve the transient response of the platoon control. Two robust platoon control methods, a nonlinear disturbance observer (DOB)-based ISMC and an adaptive dual-layer super-twisting ISMC (STISMC), are proposed. The proposed methods based on the nonlinear DOB and ISMC are more effective against highly nonlinear disturbances than the existing linear DOB-based methods and the disturbances can be compensated at all times by eliminating the reaching mode, unlike the exiting SMC-based methods. Furthermore, the proposed adaptive dual-layer STISMC provides a continuous control input that guarantees the finite time convergence of the sliding mode variables to zero without requiring an upper bound on disturbance. Both simulation and experimental results are provided to demonstrate the superior platoon control performance of the adaptive dual-layer STISMC method compared to both the existing linear DOB-based robust platoon control method using ISMC and the nonlinear DOB-based ISMC method.

Centralized Model Predictive Control With Human-Driver Interaction for Platooning

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. As a novel concept, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study, demonstrating that the safety distance, velocity, and actuation constraints are obeyed. Additionally, in simulation we illustrate a key property of string stability where the impact of a disturbance is reduced through the platoon.

Developing a new integrated advanced driver assistance system in a connected vehicle environment

Advanced driver assistance systems (ADASs) can effectively enhance driving and safety performance. Due to the inherent limitations of in-vehicle technologies concerning information sharing, existing studies mainly focus on demonstrating the effectiveness of onboard sensor-based individual ADAS functions rather than their collaborative effectiveness. Thanks to the emerging connected vehicle (CV) technologies, it is viable to physically realize the collaboration and coordination between different ADAS functions. This study aims to synthesize seven ADAS functions into an integrated advanced driving assistance system (iADAS) in a CV environment. The seven ADAS functions include omni-directional collision warning, lane-change warning, curve speed warning, emergency event notification, car-following guidance, identification of variable speed limits, and information services. The activation indicators and activation conditions of these ADAS functions are derived based on a single local coordinate system. This derivation has considered the real-time motion states of the ego and surrounding vehicles. Different ADAS functions are classified based on their roles in accident reduction, traffic efficiency enhancement, and driver convenience improvement. Afterwards, their priority of releasing information is set by considering the classification and primary functionality. Finally, the effectiveness of the iADAS is validated in field tests. Testing results reveal that the iADAS helps reduce rear-end collisions and prevent rollovers or sideslips on curved roads. Furthermore, younger drivers respond faster with higher driving stability regarding lateral collision warnings. Young, well-educated, and low-risk taking drivers maintain a short but safe time headway to the leading vehicle.

Model predictive control of multi-objective adaptive cruise system based on extension theory

Under certain working conditions, the car-following performance and longitudinal ride comfort of adaptive cruise control (ACC) vehicles are contradictory. Therefore, the extension coordinated control is introduced into the weighted design of each performance index under the model predictive control (MPC) framework to optimize the overall vehicle driving performance. In this article, the dynamic model of the ACC vehicle and the variable time headway model are established, and then the predictive model and its corresponding cost function under the MPC framework are designed. By using the co-simulation platform of CarSim and Matlab/Simulink, three different simulation conditions are established and compared with the traditional ACC operating results. It was determined that the tracking speed error in the acceleration stage can be reduced by approximately 40% and the acceleration amplitude can be reduced by between 8%–17%. Therefore, there is an optimization effect under this control method. This study provides a foundation for curving ACC under an extension coordinated control theory.

Research on Ecological Driving Following Strategy Based on Deep Reinforcement Learning

Traditional car-following models usually prioritize minimizing inter-vehicle distance error when tracking the preceding vehicle, often neglecting crucial factors like driving economy and passenger ride comfort. To address this limitation, this paper integrates the concept of eco-driving and formulates a multi-objective function that encompasses economy, comfort, and safety. A novel eco-driving car-following strategy based on the deep deterministic policy gradient (DDPG) is proposed, employing the vehicle’s state, including data from the preceding vehicle and the ego vehicle, as the state space, and the desired time headway from the intelligent driver model (IDM) as the action space. The DDPG agent is trained to dynamically adjust the following vehicle’s speed in real-time, striking a balance between driving economy, comfort, and safety. The results reveal that the proposed DDPG-based IDM model significantly enhances comfort, safety, and economy when compared to the fixed-time headway IDM model, achieving an economy improvement of 2.66% along with enhanced comfort. Moreover, the proposed approach maintains a relatively stable following distance under medium-speed conditions, ensuring driving safety. Additionally, the comprehensive performance of the proposed method is analyzed under three typical scenarios, confirming its generalization capability. The DDPG-enhanced IDM car-following model aligns with eco-driving principles, offering novel insights for advancing IDM-based car-following models.

Estimation of system delay based toll equivalency factors at toll plazas using simulation

The service time and time headway of the vehicles are used to define the equivalency factor of different vehicle classes at the toll plaza. Both the service time and time headway are point measures and do not account for the system delay (time difference when a vehicle enters the queue and leaves the tollbooth) caused to the vehicle. The present study aims to quantify the system delay incurred to the vehicles at electronic toll collection (ETC) lanes under mixed traffic conditions using a microsimulation approach. Field data collected from one toll plaza located on a National Highway are used for simulation model generation. A new terminology called system delay-based toll equivalency factor (DTEF) is introduced to convert the different vehicle classes into equivalent passenger cars. The DTEF variation for different approach volumes and heavy commercial vehicle (HCV) compositions were checked at different ETC penetration levels. A total of 288 scenarios have been worked out, and simulation runs have been made for all such scenarios to obtain the DTEF values. The average DTEF value of HCV was obtained as 2.20. Further, it is found that with an increase in approach volume and HCV share in the traffic stream, the DTEF value increases. The outcome of the present study will be useful to field practitioners and engineers to determine the capacity in equivalent DTEF/hr and level of service of a toll plaza in terms of system delay.

Adaptive Fuzzy Predefined-Time Control for Third-Order Heterogeneous Vehicular Platoon Systems With Dead Zone

This paper investigates the problem of fuzzy adaptive predefined-time terminal sliding mode (TSM) control for a third-order heterogeneous vehicular platoon system (HVPS) with an unknown dead-zone. For the purpose of approximating unknown nonlinear functions, fuzzy logic systems (FLSs) are utilized. Additionally, the impact of the dead-zone on the performance of the control may be lessened by building the dead-zone compensation. A tracking error based on the modified constant time headway (MCTH) policy is built to get rid of the assumption of zero initial spacing and decrease the distance between vehicles at the same time. A unique nonsingular TSM control system is then built using the predefined-time stability criterion, with the help of Lyapunov functions, it is possible to demonstrate both the individual and the string stability (SS) of the whole heterogeneous vehicle platoon in a predefined time. Finally, a series of simulations are shown to demonstrate the validity of the proposed results.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Neural Network-Based Subway Regenerative Energy Optimization With Variable Headway Constraints

For subway regenerative energy optimization, the essential idea is to maximize the amount of regenerative energy absorption (AREA) between accelerating trains and braking trains by adjusting train timetable. The bottlenecks preventing the theoretical research from being used to industrial practice lie in the time-consuming simulation of AREA and the constant setting of headway threshold, which seriously reduces the computational efficiency and compresses the solution space. For addressing these issues, we formulate neural networks to speed up the AREA simulation, develop a bisection algorithm to dynamically adjust the headway threshold under moving block mechanism, and then establish a hybrid heuristic method integrating neural networks, genetic algorithm, variable neighborhood search and simulated annealing to determine the timetable, including dwell time distribution (DTD) at stations and headway time distribution (HTD) among consecutive trains. The effectiveness of the proposed method is confirmed by numerical experiments based on the real ATO data of Beijing Subway Changping Line. The results reveal that the neural networks could save 98.96% of the computational time at the expense of 1.76% of the accuracy loss, such that the hybrid heuristic algorithm could save 75.68% of the computational time. Benchmarked with the constant headway constraints used in literature, the variable headway constraints could achieve an average improvement of AREA by 8.94%. In addition, the joint optimization of DTD and HTD could increase the AREA by 15.28% on average, compared to the single optimization of HTD. This research is of great significance to improve the practicability of subway regenerative energy optimization algorithms.

An exact optimization method for coordinating the arrival times of urban rail lines at a common corridor

The trips of a high-frequency urban rail line are typically planned with the aim of achieving even time headways. This results in reliable services for each urban rail line, where successive trips have the same time headway. Maintaining even time headways for each service line has significant advantages for the passengers of the line, but it might result in safety issues, vehicle bunching, and increased transfer times at a common corridor served by multiple urban rail lines. This study investigates the problem of urban rail corridor coordination and develops an exact optimization method for coordinating the vehicle trips of different lines that serve stations along a joint corridor. The proposed formulation is a non-convex mathematical program which is reformulated as a mixed-integer quadratic program with a convex objective function. A branch-and-bound algorithm coupled with the Active-set method is proposed for solving the model to global optimality. Results from a toy network and a case study of the light rail service in The Hague, The Netherlands, demonstrate the improvement potential of time headways at a common corridor, while accounting for the effect on the variation of time headways at isolated segments of the individual service lines.

Research on V2V Time Headway Strategy Considering Multi-Front Vehicles Based on Artificial Potential Field

Within the Internet of Vehicles, the driver’s conduct is more affected by the information about the vehicles in front of them. To meet the driver’s demand for the design of the safety vehicle distance of the adaptive cruise control system in the Vehicle-to-Vehicle (V2V) environment, this paper employs the artificial potential field (APF) theory to enhance the vehicle time headway strategy and constructs a mixed force function in the global force field with three front vehicles as the center of the three potential fields. Taking into account the inadequate control effect of conventional APF in a dynamic environment, this paper describes the relative kinematic characteristics of the vehicles in front and the host vehicle as parameters and obtains the improved APF. The improved strategy based on APF enhances the forward-looking and anti-interference ability of the distance strategy. Also, the strategy presented in this paper can achieve time headway control under all operating conditions and prevent acceleration fluctuations due to switching between various control strategies. For collaborative simulation, this research uses MATLAB/Simulink and Carsim software. The simulation is conducted under three common traffic scenarios, and it is determined that the improved control method results in smoother changes in vehicle speed, acceleration, and car-following distance, hence improving driving and riding comfort.

Energy-efficient adaptive cruise control system with acceleration prediction via long short-term memory

An energy-efficient adaptive cruise control (ACC) system that predicts the preceding state is proposed to meet the demands of driving in different states and working conditions. The long short-term memory (LSTM) network predicts the trajectory of the preceding vehicle’s future acceleration, allowing for an adaptive time headway that takes acceleration into account. The acceleration of the preceding vehicle is also incorporated as an augmented state in the deep deterministic policy gradient (DDPG) algorithm, resulting in a combined algorithm called prediction deep deterministic policy gradient (PDDPG). A multi-objective reward function is constructed based on human driving data to evaluate the performance indexes of vehicle longitudinal control, including efficiency, safety, and economy. The rules for changing the weight of each target performance under various typical cycle conditions are determined through experiments. The Carsim-based urban, suburban and highway conditions together with the Simulink vehicle dynamics model are compared with human driving data and ACC of conventional algorithms. Based on the test results, the proposed algorithm can improve fuel efficiency by 19.36% in urban driving conditions and reduce acceleration fluctuations.

Characteristics of pedestrians’ alighting and boarding process in metro stations

The alighting and boarding process in metro stations is complicated, and it is impacted by pedestrians’ different behaviours. This paper aims to study the characteristics of pedestrians’ alighting and boarding process in metro stations through a controlled experiment. In this experiment, there are three variables, namely the ratio of the number of boarding pedestrians to alighting pedestrians (i.e., 1:3, 1:1 and 3:1), whether (i.e., yes and no) obeying the rule of “Disembarking precedes embarking” and the number of pedestrians in the room. Video recordings and the PeTrack software are employed to extract and track pedestrians’ trajectories. Time headway, detouring degree, speed and difference in boarding and alighting strategies are analyzed according to the trajectories. It is discovered that the time headway and detouring degree of pedestrians in tests that pedestrians disobey the “Disembarking precedes embarking” rule is higher than that pedestrians obey the rule under the same number of on-board pedestrians and ratio of the number of boarding pedestrians to alighting pedestrians, while the average speed is the opposite. If the number of alighting pedestrians and boarding pedestrians is equal, i.e., 1:1, the case that pedestrians disobey the “Disembarking precedes embarking” rule has the most significantly negative impact on time headway, detouring degree and speed. The boarding pedestrians board from the left side of the door can improve their boarding efficiency, particular under the high number of boarding pedestrians. It is beneficial for alighting pedestrians to alight from the left side of the door if the ratio of the number of boarding pedestrians to alighting pedestrians is 1:1. It is hoped that our findings will be helpful in improving alighting and boarding efficiency and protecting pedestrian safety in metro stations.

DMPC-based string stable platoon control with robustness against communication delays

Communication delays are inevitable in vehicle platoons and may inhibit control performance. To address this issue, existing delay compensation strategies often resort to a greater time headway based on the delay upper bound, which compromises the benefits on traffic throughput. Moreover, it remains a major challenge to strictly ensure string stability with delays for distributed model predictive control (DMPC)-based controller. This paper presents a DMPC method for the cooperation of heterogeneous platoons subject to time-varying communication delays and greatly improves robustness against communication delays by improving the DMPC mechanism. A third-order heterogeneous vehicle dynamics model is integrated into the proposed method to improve control precision when implemented in the field. A synchronization mechanism is designed and the constant spacing policy is applied, which eliminates the delay dependence of the time headway selection and may thus improve traffic throughput. By incorporating a dual-mode scheme, a robustness constraint and a string stability constraint in the optimization problem, feasibility, closed-loop stability, and string stability are theoretically proved. A sufficient condition on the controller parameters is derived to guarantee closed-loop stability under communication delays. Numerical simulations are conducted to demonstrate the effectiveness of the proposed delay-compensating DMPC controller. The results reveal that the proposed controller outperforms the traditional DMPC controller and delay-involved linear controller on string stability, tracking performance and desired spacing selection.

A car-following model based on the optimized velocity and its security analysis

Abstract An enhanced optimal velocity model (EOVM) that considers driving safety is established to alleviate traffic congestion and ensure driving safety. Time headway is introduced as a criterion for determining whether the car is safe. When the time headway is less than the minimum time headway (THmin ) or more than the most comfortable time headway (THcom), the acceleration constraints are discussed to ensure the model’s safety and maintain the following state. A stability analysis of the model was carried out to determine the stability conditions of the model. The EOVM is compared with the optimal velocity model (OVM) and fuzzy car-following model using the real dataset. Experiments show that the EOVM model has the smallest error in average, maximum and median with the real dataset. To confirm the model’s safety, design fleet simulation experiments were conducted for three actual scenarios of starting, stopping and uniform process.

Optimal convoy composition for virtual coupling trains at junctions: A coalition formation game approach

This paper presents a virtual coupling (VC) train convoy composition optimization framework to create a multiple formation structure maximizing the capacity benefit while considering transportation requirements and system limitations. First, the operational constraints of VC trains in the convoy formation process are mapped as headway time transitions. A coordination strategy is proposed by utilizing the natural deceleration process at junctions to minimize the negative effects of the coupling process. Multiple composition structures are created by forming varied potential convoys that adhere to these constraints and transportation schedules. Second, a utility function for convoy composition is modeled using the coalition formation game theory to quantify the operational efficiency of VC trains passing through bottleneck sections. It reveals that coupling time and convoy structure are interdependent factors that jointly impact the capacity benefits. Thirdly, a train convoy formation algorithm is proposed to determine the optimal convoy composition by comparing formation structures, designing preference relations, and estimating lower bounds. This algorithm guarantees the convergence of the optimal structure result. Finally, the proposed framework is evaluated on four typical rail transit market segments and transportation organization scenarios. The application results demonstrate that the optimized convoy structures increase capacity benefits in the four typical market segments by 26.1%, 30.7%, 24.7%, and 18.9%, respectively. VC improves the capacity of the bottleneck sections by increasing the operating density. The present study lays a theoretical foundation for strategic-level applications of virtual coupling.

Freeway capacity modeling and analysis for traffic mixed with human-driven and connected automated vehicles considering driver behavior characteristics

Future freeway traffic flow will include both Human-driven Vehicles (HV) and various sizes of Connected Automated Vehicles (CAV) platoons. When following CAV platoons, HV drivers may change the car-following distance for psychological reasons, which could further affect the road capacity under mixed traffic flow. In this paper, we constructed a capacity model that considered the spatial distribution characteristics of mixed traffic flow and the behavioral traits of HV drivers when they follow CAV platoons. We surveyed 331 HV drivers using a questionnaire. 51.1% of drivers opted to increase the car-following distance when following a CAV platoon, and the amount of change in distance increased in direct proportion to the size of the platoon. A Structural Equation Model (SEM) based on survey data was developed to examine the connection between psychological variables and HV drivers’ driving behaviors. These results of the questionnaire were introduced into the capacity model construction, and the trends of capacity with CAV penetration, maximum platoon size, platoon intensity, and time headways were revealed. The results show that previous studies overestimated the capacity under mixed traffic flow, especially when the maximum platoon size is equal to or greater than 6 or the CAV penetration is between [0.6, 0.8], the overestimation of the capacity is higher. CAV penetration, maximum platoon size, and platoon intensity are positively correlated with capacity, while time headway is negatively correlated with capacity. The capacity growth is not significant when CAV penetration is between (0, 0.4]. Six is the ideal size of CAV platoons under mixed traffic flow when considering the impact of CAV platoons on HV drivers’ driving behaviors. By taking measures to promote CAV aggregation and making platoon intensity greater than 0, the capacity will be improved more significantly. For a specific car-following type, a different value of time headway will lead to a different capacity, but the impact trends will be similar.

Robust String Stability and Safety of CTH Predictor-Feedback CACC

We establish robustness of string stability to delay uncertainty as well as positivity of spacing and speed states, for homogeneous vehicular platoons under predictor-feedback Cooperative Adaptive Cruise Control (CACC). Each individual vehicle’s dynamics are described by a second-order linear system with delayed desired acceleration, under acceleration information transmitted to the ego vehicle from a single, preceding vehicle. The nominal design (in the delay-free case) is a constant time-headway (CTH) policy and no restriction on the delay size, in relation with the desired time headway, is imposed. The proofs rely on combination of an input-output approach (on the frequency domain) and on deriving estimates on explicit, closed-loop solutions; under specific, sufficient conditions that are derived on initial conditions and parameters of the baseline, CTH controller. We illustrate in simulation and numerical examples, the guarantees of robust stability and string stability as well as of collisions avoidance, of CTH predictor-feedback CACC design. We also present extensions of our design and analysis approach to heterogeneous, third-order dynamic models of vehicles.