•In-vehicle RLR collision warning timing was tested in a driving simulator.•The range of warning lead time is from 2.5s to 5.5s with 0.5s increment.•Collision avoidance performances are highly ...affected by warning timings.•Earlier warning leads to shorter reaction time and lower deceleration rate.
Collision warning systems have been identified as an effective technique for avoiding accidents. In such a system, the delivery time of warning messages is a crucial factor that influences the success of collision avoidance. This study therefore contributes by providing experimental analyses on a range of delivery times of warning messages, which has been overlooked in past studies. Using simulator-based techniques, experimental scenarios are specifically designed for accounting the red-light-running events at intersections and drivers are recruited to test on different settings of warning timings. Several measures including brake reaction time, alarm-to-brake-onset time and deceleration are adopted as reflections of drivers’ performances under the collision avoidance process and they are connected to several factors by mixed effect models. According to the results, the collision warning system actually can largely reduce the occurrence of red-light-running collisions, more importantly it reveals the influence of warning timings within the predefined ranges and 4.0s or 4.5s may be a proper warning timing for the right-angle collisions accused by red-light-running vehicles in this study. Besides, effects from directional information embedded in warning messages are also investigated in this study. Findings are important to the design of collision warning systems especially in the aspect of warning timings.
•We describe a MRZINB model for jointly modeling crash counts.•The proposed model can handle excess zeros in correlated crash data.•The proposed model can account for unobserved ...heterogeneity.•Compared to the MZINB, the MRZINB model provides better goodness of fit.
Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.
Traffic assignment and management objectives are considered as two significant parts in developing the emergency evacuation plan, which can directly influence the evacuation performance and ...efficiency. From the perspective of disaster response operators, the evacuation objective frequently is to minimize the total evacuation time to reduce losses, which may lead to an unreasonable and unfair phenomenon where people in highest risk areas may be forced to sacrifice their priorities of evacuation to improve the system evacuation efficiency. In this paper, considering both efficiency and social fairness in emergency evacuation, a weight function consisting of risk evaluation index as variable and the emphasis degree of managers on social fairness principle as coefficient was initially proposed and embedded in system optimal (SO) objective function. Combining the weight function and other constraints based on an extended cell transmission model (CTM), the linear program (LP) model was established to realize the simulation of dynamic traffic assignment in emergency evacuation. Employing this model, the impact of the management strategy of balancing both efficiency and social fairness on evacuation results was studied in the "Tianjin Explosions" case. In the end, the conclusion of "balancing social fairness is valuable during evacuation" was obtained.
•We proposed a hierarchical method to examine fog influence on speed behaviors.•In foggy conditions, drivers reduced speeds in order to lower the driving risk.•Drivers could not response timely to ...the impending changes in road geometry in fog.•Drivers’ speed compensation could not sufficiently reduce the crash-involved risk.
Driving in foggy weather is a potentially dangerous activity that has been investigated using various approaches. However, most of the previous research has focused on the driver’s response in a particular situation under foggy conditions and lacks a systematic analysis of driving performance, especially speed-related behaviors. This paper presents a hierarchical driving performance assessment method to investigate the effects of foggy conditions on drivers’ speed control behaviors. With this method, driving behaviors were tested in three simulated driving scenarios classified into three risk levels: basic speed control while driving along road segments at a low risk level, dynamic speed adjustment in car-following situations at a medium risk level, and emergent speed responses to precrash situations at a high risk level. The driving simulation experiment results indicated that the drivers intended to reduce their speeds in order to lower the driving risk in foggy conditions at all three risk levels. However, due to the limited visibility in fog, the drivers could not observe and respond to impending changes in road geometries in a timely way, which resulted in higher operating speeds than in clear weather conditions. At the medium risk level, the drivers’ dynamic speed adjustment behavior was degraded in the fog, with both acceleration and deceleration rates lower than in the clear conditions; therefore, more rear-end collisions happened in the foggy conditions than in the clear conditions. At the high risk level, the experiment results showed that drivers’ speed compensation in foggy conditions does not sufficiently reduce their crash-involvement risk, but it can effectively lower the crash severity, as indicated by a significantly lower collision speed.
•We established two typical scenarios in a driving simulator, including the yellow indication judgment scenario and the collision avoidance scenario.•Non-professional drivers paid more attention to ...red light running violations than taxi drivers.•Taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision.•Non-professional drivers had more abrupt deceleration behaviors when facing a potential crash.
Due to comfort, convenience, and flexibility, taxis have become increasingly more prevalent in China, especially in large cities. However, many violations and road crashes that occurred frequently were related to taxi drivers. This study aimed to investigate differences in driving performance between taxi drivers and non-professional drivers from the perspectives of red-light running violation and potential crash involvement based on a driving simulation experiment. Two typical scenarios were established in a driving simulator, which includes the red-light running violation scenario and the crash avoidance scenario. There were 49 participants, including 23 taxi drivers (14 males and 9 females) and 26 non-professional drivers (13 males and 13 females) recruited for this experiment. The driving simulation experiment results indicated that non-professional drivers paid more attention to red-light running violations in comparison to taxi drivers who had a higher probability of red-light running violation. Furthermore, it was found that taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision and non-professional drivers had more abrupt deceleration behaviors when facing a potential crash. Moreover, the experiment results showed that taxi drivers had a smaller crash rate compared to non-professional drivers and had a better performance in terms of crash avoidance at the intersection.
•Several effects of situation urgency on brake response time, previously observed separately, were replicated in one single simulator study.•Mechanistic models of brake timing, based on visual ...looming, were fitted to the human response times.•A looming threshold model was not able to capture the distribution of brake response times.•A model accumulating evidence from both visual looming and brake light onset provided the best fit to the data.•All models fitted the data better when quantifying looming cues as relative optical expansion rather than as optical expansion directly.
Previous studies have shown the effect of a lead vehicle’s speed, deceleration rate and headway distance on drivers’ brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle’s speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver’s retina, and inverse tau τ-1, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ-1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ-1.
Using the classification and regression tree (CART) method, this study aims to model the vehicle merging behavior at work zone merging areas during the merging implementation period. Hereafter, the ...merging implementation period is defined as the period from the starting time to the completion time of a merging maneuver. From the safety perspective, the times to collision (TTC) between the merging vehicle and its neighboring vehicles are regarded as the factors affecting vehicle merging decisions. The results show that a larger delay, a shorter remaining distance to the work zone, a smaller TTC to the merging lead vehicle, and a higher merging vehicle speed may encourage drivers to complete merging maneuvers early. It is also found that the merging vehicle tends to continue merging when the TTCs between the merging vehicle and its neighboring vehicles in the through lane are too small. In addition, another CART model without the use of TTC is built for comparison. The finding indicates that the use of TTC can not only contribute to a perfect result but also highlight the merging safety situation more clearly. The merging rules of this study are ready to be incorporated into the merging assistance system for guiding a safety merging at work zone merging areas.
The mobility of modern metropolises strongly relies on urban rail transit (URT) systems, and such a heavy dependence causes that even minor service interruptions would make the URT systems ...unsustainable. This study aims at optimally dispatching the ground feeder-bus to coordinate with the urban rails' operation for eliminating the effect of unexpected service interruptions in URT corridors. A feeder-bus dispatch planning model was proposed for the collaborative optimization of URT and feeder-bus cooperation under emergency situations and minimizing the total evacuation cost of the feeder-buses. To solve the model, a concept of dummy feeder-bus system is proposed to transform the non-linear model into traditional linear programming (ILP) model, i.e., traditional transportation problem. The case study of Line #2 of Nanjing URT in China was adopted to illustrate the model application and sensitivity analyses of the key variables. The modeling results show that as the evacuation time window increases, the total evacuation cost as well as the number of dispatched feeder-buses decrease, and the dispatched feeder-buses need operate for more times along the feeder-bus line. The number of dispatched feeder-buses does not show an obvious change with the increase of parking spot capacity and time window, indicating that simply increasing the parking spot capacity would cause huge waste for the emergent bus utilization. When the unbalanced evacuation demand exists between stations, the more feeder-buses are needed. The method of this study will contribute to improving transportation emergency management and resource allocation for URT systems.
Abstract Online car‐hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the ...particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car‐hailing tourists is relatively lacking at this stage. Based on multi‐source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short‐term memory–convolutional neural network (BO‐LSTM‐CNN) method to conduct multi‐task online car‐hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO‐LSTM‐CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi‐source information related to weather, space, and online car‐hailing attributes. Extracting online car‐hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car‐hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel.
•The car–truck following pattern has the largest rear-end crash risk, followed by truck–truck, truck–car and car–car patterns.•The separate rear-end crash risk model has been developed for each ...vehicle-following pattern.•The effects of influencing factors on rear-end crash risk are found to vary according to the vehicle-following patterns.
This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car–car, car–truck, truck–car and truck–truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car–truck following pattern has the largest rear-end crash risk, followed by truck–truck, truck–car and car–car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car–car pattern, lane position has less effect on rear-end crash risk in the car–truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck–car pattern.
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