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Time headway analysis is crucial in traffic engineering for understanding traffic flow and enhancing safety. Prior studies often neglected the impact of curbside bus stops, but this study explores time headway in vehicular traffic with curbside bus stops, estimating critical time headway using fundamental diagram-derived road section capacity. Field data was collected from various sections, both with and without curbside bus stops, experiencing varying traffic conditions. Headway data analysis showed curve profiles changing due to lane shifts, traffic flow, and interactions between leading and following vehicles. Various factors, including traffic volume, two-wheeler percentage, and lane count, all negatively affected average time headway. In contrast, longer bus dwell times and more heavy vehicles in the traffic stream led to increased time headway across all road sections. A model was proposed to estimate average time headway based on these influencing variables. Moreover, a speed-volume diagram assessed road capacity, revealing critical time headway variations: 0.63 seconds for six-lane divided roads and 1.43 seconds for four-lane divided roads. These differences stemmed from factors including traffic flow, road design, driver behavior, and capacity considerations. The study establishes critical time headway’s vital role in improving safety, reliability, and efficiency at curbside bus stops.

Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

Roundabouts are a prevalent type of intersection known for their potential to enhance traffic flow. Ensuring their effective design is crucial for optimizing traffic performance. This study focuses on evaluating the capacity of roundabouts, essential for both planning new installations and assessing existing ones. Field data from thirteen roundabouts in Hungary were analyzed to estimate critical gap and follow-up headway values for each entry. Employing Raff's graphical method, critical gap values were determined, while follow-up headway was calculated by averaging the time taken for two waiting vehicles to accept the same gap over eight instances. The critical gaps and follow-up headway values for all forty-one entries ranged between 2.41–3.46 s and 1.8–2.4 s, respectively. Subsequently, the Highway Capacity Manual (HCM) roundabout capacity equation was calibrated using these gap acceptance parameters. The proposed model yielded higher entry capacity (1,672 PCU/h) compared to the HCM model. Validation against actual field entry capacity values demonstrated a strong correlation (R2 = 0.94), affirming the model's accuracy. Comparisons with international models, such as HCM 2016, Brilon-Wu, and Brilon-Bondzio, revealed the superiority of the proposed model in terms of entry capacity (1,672 PCU/h versus 1,380 PCU/h, 1,241 PCU/h and, 1,218 PCU/h respectively).

Protonation of methane (CH4), a rather rigid molecule well described by quantum mechanics, produces CH5+, a prototypical floppy molecule that has eluded definitive spectroscopic description. Experimental measurement of high-resolution spectra of pure CH5+ samples poses a formidable challenge. By applying two types of action spectroscopy predicated on photoinduced reaction with CO2 and photoinhibition of helium cluster growth, we obtained low-temperature, high-resolution spectra of mass-selected CH5+. On the basis of the very high accuracy of the line positions, we determined a spectrum of combination differences. Analysis of this spectrum enabled derivation of equally accurate ground state–level schemes of the corresponding nuclear spin isomers of CH5+, as well as tentative quantum number assignment of this enfant terrible of molecular spectroscopy.

IntroductionThe time headway of vehicles is of fundamental importance in traffic engineering applications like capacity, level-of-service and safety studies. Further, the performance of traffic simulation depends on inputs into the simulation process and ‘accurate vehicle generation’ is critical in this context. Thus, it is important to define headway distribution pattern for the purpose of analyzing traffic and subsequently, taking infrastructure related decisions. In so far, majority of the researches on this subject are based on homogeneous traffic and effects of mixed traffic especially on two-lane roads are yet to be culminated. The present study, thus, aimed at investigating headway distributions on such roads under mixed traffic situation.MethodsField study was conducted on two-lane highways in India that exhibits heterogeneity in its traffic composition. Contestant headway distribution models were evaluated and four distribution functions namely, log-logistic, lognormal, Pearson 5 and Pearson 6 were considered while modeling the headway data. The appropriate models were selected using a methodology based on K-S test and subsequent field validation.ResultsLog-logistic distribution was found appropriate at moderate flow whereas, at congested state of flow it was Pearson 5. However, at unstable flow nearing capacity, both, following and non-following components of headways were observed to follow different distributional characteristics. Nomographs are developed for calculating the headway probabilities at different flow levels considering the appropriate distribution models. ‘Probability of headway less than‘t’s’ increases with the flow rate and rate of such increase is considerably high for headways 7.5 s or more. This attributes to the fact that at heavy flow more vehicles are entrapped inside platoons and they move in following with shorter headways. Further, a comparison was made between the headway probabilities obtained in the current study and road segments that exhibit more or less homogeneous traffic. It was found that at moderate flow level proportion of shorter headways are considerably high under mixed traffic.ConclusionsThe present paper demonstrates the effect of mixed traffic on distribution of time headways of two-lane roads. Presence of slower vehicles in such traffic leads to frequent formation of platoons, thereby, increases the risk taking behavior of drivers’ while overtaking. As a result, proportion of shorter headways increases resulting in highly skewed observations. Thus, the study proposed Log-logistic distribution under medium flow since it can model events which have ‘increased initial rate’ and Pearson 5 under heavy flow to model ‘highly skewed data’. The model outputs were accordingly compared with other studies and were found to explain the mixed traffic characteristics satisfactorily. The present study, thus, creates a starting point of further initiatives aimed at establishing a robust method of modeling headways on two-lane roads with mixed traffic.

Traffic flow will harmonize to forward conditions. The time and distance required for harmonization can have a significant effect on the traffic density behavior. The flow can evolve into clusters of vehicles or become uniform depending on parameters such as safe time headway and safe distance headway. In this paper, a new model is presented to provide a realistic characterization of traffic behavior during the harmonization period. Results are presented for a discontinuous density distribution on a circular road which shows that this model produces more realistic traffic behavior than other models in the literature.

Uphill and downhill roads are prevalent in mountainous areas and freeways. Despite the advancements of vehicle-to-vehicle (V2V) communication technology, the driving field of vision could be still largely limited under such a complex road environment, which hinders the sensors from accurately perceiving the speed of front vehicles. As such, a fundamental question for autonomous traffic management is how to control traffic flow associated with the velocity uncertainty of preceding vehicles? This paper aims to answer this question by devising a cooperative control method for autonomous traffic to stabilize the traffic flow under such a complex road environment. To this end, this paper first develops a traffic flow model accounting for the uncertainty of preceding vehicle’s velocity on gradient roads and further devises a new self-delayed feedback controller based on the velocity and headway differences between the current time step and historical time step. The sufficient condition where traffic jams do not occur is derived from the perspective of the frequency domain via Hurwitz criteria and H ∞ norm of transfer functions. The Bode diagram reveals that the robustness of the closed-loop traffic flow model can be significantly enhanced. Simulation results show that the key parameters (control gain coefficient and delay time) of the designed controller contribute to the stability of traffic flow, which is consistent with the theoretical analysis conclusion.

In this study, a car-following model is put forth to explain the car-following behavior of HDVs (human-driven vehicles), AVs (autonomous vehicles) and CAVs (connected and automated vehicles) in mixed traffic. Based on the IDM (intelligent-driver model) and molecular dynamic theory, the model includes the velocity of surrounding vehicles along with the difference of velocity and the headway between each pair of vehicles. The drivers’ sensitivity to the deceleration of the nearest front vehicle was considered in HDV car-following modeling and the influences of various kinds of nearest front and rear vehicles were distinguished in the CAV model. Based on the information obtained from the real road test mixed with HDVs, AVs and CAVs, the optimum value of the model parameters was obtained and the accuracy of model was verified with simulation. The results show that the simulation for HDVs’, AVs’ and CAVs’ car-following behavior under the proposed model is more accurate than that for the IDM, ACC (adaptive cruise control) model and CACC (cooperative adaptive cruise control) model. The model can be applied to car-following simulations of HDVs, AVs and CAVs in mixed traffic, in the model the tools and concepts of nonlinear dynamics especially molecular dynamic theory is applied which can benefit road traffic vehicle interaction analysis. In addition, this study provides valuable suggestions and guidance for effectively guiding AVs and CAVs to follow vehicles and improving the stability of car-following behavior.

In order to improve the calculation accuracy and popularity of intersection lane saturated flow rate, we propose a calculation method of lane saturated flow ratebased on asphalt material. Firstly, environmentally friendly modified asphalt material is used to pave the intersection lanes to improve the smoothness and friction of the road surface.Then by millisecond level electric alarmvehicle data for measuring basis, using the small time interval method to extract thedata of green light saturated time interval.By K-means algorithm clustering analysis, the percentage of different time headway clusters after clustering is analyzed, and the average value of time headway of representatives group as saturated time headway, and the saturated flow rate in the measured period is further calculated.Finally, an example analysis is carried out on the vehicle data of Eshan Road and Huajin South Road in Wuhu City. The saturated flow rate calculated by the proposed method is 1478.68veh/h. Compared with the manual observation method, the relative error is less than 3%, which verified the accuracy and effectiveness of the proposed method.

In order to ensure the high-quality operation of urban traffic, the impact of rainfall on urban road characteristics was studied. Taking Zhengzhou as an example, the traffic volume, headway time and vehicle start loss time under different rainfall were counted. By studying the mechanism of the basic capacity and actual capacity of roads affected by rainfall, the changes in the degree of impact of rainfall on roads and other traffic environments are determined.