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At urban intersections, conflicts between right-turn vehicles and through non-motorized vehicles are a critical cause of traffic congestion and safety challenges. Based on the fact that in different countries there is no strict priority in conflicts between motorized and non-motorized vehicles, this study focused on analysis of the inherent mechani...
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... respectively. They are the bridges between safety and efficiency and their corresponding driving forces. Generally, the safer the driver is, the safer the driver feels (the higher his/her perception of safety), the weaker his/her safety driving force will be, and the less motivation he/she has to take actions to improve his/her safety. Fig. 2 shows the process influencing safety, perception of safety, and the safety driving force: the changes in safety affect the driver's perception of safety, and the changes in perceived safety stimulate the safety driving force to change, resulting in actions taken to change the driver's own situation and thus to influence his/her safety, ...
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... types of driving force depend on the types of personal aim. Specifically, for a driver, safety and efficiency are his/her two uppermost aims during travel (see, e.g., Wang et al., 2002;Schmidt-Daffy, 2012. Therefore, the safety and efficiency driving forces are the two kinds of foremost driving forces for a driver. They represent the driver's motivation to improve his/her safety and efficiency, respectively. However, drivers cannot know the objective value of their safety and efficiency, but they can perceive the degree to which they are safe and efficient. This leads to another important concept: the perception of safety and efficiency. A driver's perception of safety and efficiency is his/her psychological evaluation of the fulfillment of the aims of safety and efficiency, respectively. They are the bridges between safety and efficiency and their corresponding driving forces. Generally, the safer the driver is, the safer the driver feels (the higher his/her perception of safety), the weaker his/her safety driving force will be, and the less motivation he/she has to take actions to improve his/her safety. Fig. 2 shows the process influencing safety, perception of safety, and the safety driving force: the changes in safety affect the driver's perception of safety, and the changes in perceived safety stimulate the safety driving force to change, resulting in actions taken to change the driver's own situation and thus to influence his/her safety, and so on. The process for efficiency is the same. Note that in the case of right-turn behavior, the actions taken include acceleration and ...
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Citations
... SFR can be obtained by 3600 s dividing the mean saturation headway. The value of SFR can be influenced by many factors such as vehicle compositions (e.g., percentage of buses, heavy vehicles), interference from parking vehicles, non-motorized vehicles, and pedestrians Allen et al., 1998;Lin et al., 2016). In field investigation of a fixed movement, the observed SFR can vary a lot because of the continuously changing traffic conditions. ...
The network's admissible demand region (ADR), which is a key index to characterize a network's ability to handle incoming demands, is shaped by each movement's saturation flow rate (SFR). Existing backpressure (BP) traffic control policies commonly assumed a fixed and/or completely known SFR when calculating the pressure for decision-making. On one hand, since real-time traffic conditions can significantly influence the traffic supply, the fixed mean SFR (M-SFR) assumption could result in a mismatch between dynamic demand and supply. On the other hand, accurately predicting the imminent SFR (I-SFR) is challenging because of the complicated interactions between traffic participants. Hence, the completely known SFR assumption is impractical in real-world settings. Our paper demonstrates that, compared with only using the constant M-SFR information, using more knowledge of I-SFR can enlarge the upper bound of ADR. In addition, we theoretically prove that the BP with predicted I-SFR can guarantee network stability as long as the demand is interior to the ADR. The proposed theory is validated by a calibrated simulation model in the experiments. Three I-SFR prediction methods with different accuracies are adopted: the M-SFR method, the heuristic estimation method, and the deep neural network method. They are tested in three BP-based control policies to investigate whether our findings are robust. The simulation results show that: a higher prediction accuracy of I-SFR can effectively help all three BP-based policies enlarge the network ADR, and more accurate I-SFR can productively reduce the average vehicle delay.
... • Type of the two-wheeler: referred from a previous study [82], we use the coefficient S t of the type to represent the perceptual differences caused by the discrepancy of the dynamic performance of two-wheelers. As mentioned in Section 1, the e-moped riders, e-bike riders, and cyclists are the three main types of two-wheelers we considered in this study. ...
... From Tao et al. [87]'s work it can be seen that the farther the distance from the current road user to another individual, the smaller the influence brought by the individual on the current road user, and vice versa. Meanwhile, the speed of other individuals is often significant, which commonly shows proportional to their impact on the current road user [82]. However, it is worth noting that when the road user locates behind the current object, it is best to consider their speed difference [88] because this index can represent whether the road user is approaching. ...
In recent years, the interest in riding in cities using the two-wheeler (e.g., bicycles, electric bicycles, electric mopeds, etc.) increases. Mixed-traffic road segments are one of the most common traffic scenes where the mixed two-wheeler flows exist. Because the movements are often not restricted by lanes, the two-wheeler uses lateral road space more freely and shows obvious multilateral interactions (i.e. multi-interaction) with others, bringing issues that endanger traffic safety. A precise estimation of its impacts on traffic operation and safety is necessary, while the microscopic simulation model can satisfy the need as a helpful tool. However, most existing simulation models of these three types of two-wheelers are essentially focusing on handling the one-on-one interaction. The capability to deal with the two-wheeler multi-interaction in mixed traffic is still rare, and the description of what endogenous tasks are contained by the multi-interaction has also not given by literature. To this end, this paper first defines what the multi-interaction entails on the operational behaviour level, claiming that it contains three intertwined processes, namely a (mental) perception, a (mental) decision, and a physical process. The (mental) perception and decision processes represent the recognition of interactions and the response to traffic conditions, while the physical process refers to the execution of these mental activities. A three-layer simulation framework has then been developed, where each layer sequentially corresponds to one of the operational behaviour tasks. Integrated component models are also proposed in each layer to cover these operational tasks. A Comfort Zone model is hence put forward to dynamically perceive the multiple interactive road users, while a Bayesian network model is developed to deal with the decision-making process under multi-interaction situations. Meanwhile, a behaviour force model is also underlied to capture the non-lane based movements following the selected behaviour and current interaction states. Finally, we face validate the proposed models by the comparison between simulation results and observations obtained from trajectory dataset. Results indicate the model performance matches the observed interaction and motion well.
... Driving behavior research analyzes and models the driver's operation and control behavior at the theoretical level. Scholars have done a lot of work to study the driving behavior model, and have produced theoretical models, including the car following model [17]- [19], lane changing model [20]- [22], and intersection turning model [10], to realize the microsimulation of driving behavior in traffic simulations. For example, Treiber et al. [17] proposed the microscopic car following model Intelligent Driver Model, which describes the axial car-following driving behaviors according to the front vehicle's state. ...
... The lane change model [20], [21], [23] describes the probability of vehicle lane change by analyzing the surrounding traffic and road condition and models the lane change process. Lin et al. [10] proposed a driving force model to describe the non-strict priority crossing behavior of right-turn drivers. Recently, intelligent technology has been introduced into driving behavior studies. ...
Current driving behavior studies have limits in obtaining driver’s state information, and recent studies involving driver state focus on driver distraction or inattention. But it is more common that drivers operate in an intermediate subconscious state, where the drivers are neither cognitively fully focused nor distracted. There was little research to address this topic. In this study, the driver cognition alertness state information, which indicates driver subconscious alertness, is detected from eye and iris activities by non-contact computer vision methods. And a novel analysis is conducted and reveals the strong correlation between driver performance and driver cognition alertness state from experiment results. In detail, the driver cognition alertness state is quantified by the proposed metric– iris movement index, which is calculated from iris-eye relative displacements. The developed computer vision method produces high precision results in detecting faces, eyes, and irises in the experiment based on multiple deep learning networks applied in cascade, and enables displacement tracking of eye and iris targets. A filtering method is proposed and removes artifacts due to eye blinks in displacement measurements. The driving performance is estimated by a proposed performance evaluation method in a series of traffic scenarios designed with normal and emergent conditions.
... The heterogeneity in pedestrian reaction times is ignored. (Ko et al., 2012, Nguyen et al., 2012, Schönauer et al., 2012, Zeng et al., 2014, Lin et al., 2016, Zeng et al., 2017 The road user (typically a pedestrian) movement is described as a result of the sum of several attractive and repulsive social forces due to destination, the road environment, and conflicts with other road users The social force model has mostly been implemented for smaller road users like pedestrians. Applications in highspeed environments for larger road users like cars and trucks have not been investigated. ...
This thesis introduces a new Road User Safety Field Theory to proactively assess traffic safety by studying the interactions of various road users at signalised intersections. The proposed theory combines road traffic environmental factors, vehicle capabilities and personal characteristics to determine the extent and strength of road users’ safety ‘bubble’ or field across various traffic interactions. By applying the Artificial Intelligence-based video data analytics, the proposed Road User Safety Field Theory is found to better estimate crash risks in terms of crash frequency and severity than traditional traffic conflict techniques.
... Based on the research in China and internationally, eliminating the conflict in right turn lanes, designing guidelines for channelization, optimizing signal timing and setting up safety facilities are effective means to optimize intersections. Occupy parts of sidewalk space as right-turn lanes for bicycles and use barriers or road markings to isolate motor vehicles Near the intersections, guide bicycles to the sidewalk and use the sidewalk space to turn right Guide the right-turning motor vehicles to bicycle lanes and merge them with the right-turning bicycles to transfer the vehiclebicycle conflict to the road section and relieve the pressure of intersections Rationalize the turning radius to reduce the turning speed of motor vehicles and avoid conflict Near the intersections, increase the distance between bicycle lanes and vehicle lanes to prevent conflict between bicycles and right-turning motor vehicles Since the conflict between right-turn vehicles and bicycles at intersections is a key cause of traffic delays and accidents [132], it is essential to eliminate the conflict between them. At present, foreign countries mainly provide a combined bike lane/turn lane for bicyclers to reduce the conflict at the intersection. ...
Bicycle use is an important measure to promote energy saving in the urban transportation field and reduce the environmental deterioration caused by motorized vehicle development in both warm- and cold-climate cities. However, the severe winter climate makes it harder to set up bicycle lanes in cold-climate cities than in other cities. Considering international and Chinese practice, this paper analyzes bicycle travel characteristics in cold-climate cities, summarizes bicycle development patterns and proposes corresponding development strategies. The paper reviews the research status of cycling network planning and design in cold-climate cities in China and worldwide and summarizes the relevant methods that can be used in China. The results showed that compared with other seasons, in winter, the bicycle share rate of Chinese cold-climate cities decreases significantly, and the larger the temperature difference is, the greater the decrease is. In addition, the bicycle traffic flow is volatile and does not strictly maintain a regular straight line. It is necessary to select bicycle traffic development patterns and formulate corresponding strategies according to bicycle travel characteristics. Temperature and wind are two climatic factors that need special attention when planning cycling networks in cold-climate cities. The consideration of solar radiation and electric bicycles is beneficial for optimizing the design of bicycle lane width in cold-climate cities. Isolation forms can be selected according to the bicycle development patterns. The pavement materials, maintenance technology and colored bicycle lanes with anti-freezing function are very important to improve the comfort and safety of bicycle lanes.
... Most traffic managers believe that drivers should strictly give way to bicycles passing through a crosswalk [3,13,14]. However, actual investigations have found that drivers in many countries and regions, such as Norway, Finland, Germany, and China, do not strictly adhere to this rule [8,[15][16][17][18][19]. at is, during the real traffic conditions, vehicle drivers may not always assume they must comply with the rule of giving way, and instead, they compete for priority [17,18]. ...
... Most traffic managers believe that drivers should strictly give way to bicycles passing through a crosswalk [3,13,14]. However, actual investigations have found that drivers in many countries and regions, such as Norway, Finland, Germany, and China, do not strictly adhere to this rule [8,[15][16][17][18][19]. at is, during the real traffic conditions, vehicle drivers may not always assume they must comply with the rule of giving way, and instead, they compete for priority [17,18]. For example, the decision a driver makes to pass through the crosswalk or not depends on the bicycle's position and speed. ...
... In the process of vehicles passing in front of bicycles at crosswalks, drivers have only two choices: passing without stopping or stopping to give way [17]. Drivers should generally adapt their speeds to avoid endangering cyclists at crosswalks, and if necessary, drivers should stop to give way to cyclists [12]. ...
The vehicle nonstrict priority give-way behavior (VNPGWB) is a common part of traffic interaction between motorized and nonmotorized vehicles in many countries. This study proposes a mixed-flow cellular automaton model to simulate the passing of vehicles in front of bicycles at crosswalks. The mixed-flow model combines a vehicle model with a bicycle model, using nonstrict priority give-way and strict give-way two driving behaviors defined as relating to the decision point rule and the launching rule, respectively. Simulation results showed that as the vehicle and bicycle inflow rates increased, a critical inflow rate divided vehicle and bicycle traffic flow into free flow and saturated flow conditions. The values of vehicle saturation flow decreased from 0.34 to 0.05, and the values of bicycle saturation flow decreased from 0.54 to 0.44, indicating that the mixed traffic flow has a negative effect on vehicle and bicycle saturated flow. Results also showed that VNPGWB effectively improves vehicle saturation flow over that of the strict give way. The advantage of VNPGWB is more significant when vehicles and bicycles are in saturation traffic flow.
... Vehicle trajectories are very useful data for the analytics of traffic such as studying of driving behavior (Lin et al., 2016). Zaki and Sayed (2013) proposed a framework for automated road-user classification using movement trajectories, while image based methods for safety have been proposed through vehicle trajectories for crash predictions (Wang et al., 2019), and conflicts at transportation engineering facilities such as the left-turn bay (Zheng et al., 2018b). ...
The mixed traffic flow has complex dynamics by nature. The kinematic differences between automobiles and motorcycles result to distinct driving behaviors. Traditional automobile-based traffic flow theory is not always suitable for mixed traffic streams. The purpose of this study is to observe from actual data a clearance boundary, called Safety Space, drivers maintain from other vehicles, and use it as a spatial filter to determine conflicts in mixed traffic flows. Image data are collected from an Unmanned Aerial Vehicle (UAV), and microscopic characteristics such as vehicle type, position, velocity, and trajectory are extracted through computer vision techniques. The Histogram of Oriented Gradients (HOG) feature and the Support Vector Machine (SVM) classifier are utilized for the vehicle detection, while the Kalman Filter is employed for the derivation of vehicle trajectories. The Safety Space is then determined based on those trajectories. Validation data are collected at intersections in Taipei, Taiwan; Bangkok, Thailand; and Mumbai, India. The vehicle detection and tracking are satisfactory, and the Safety Space surrogate reveals risk zones caused by spatial proximity between vehicles.
... Meanwhile, the social force (SF) model has been first applied in pedestrians' walking behaviors [19,21], then in other road users' behaviors such as cyclists' conflict avoidance behaviors [25] and drivers' CF behaviors [8] and crossing behaviors at intersections [32]. ...
... Recently, researchers have extended the SF model to other road user behavior modeling and achieved good results. Coverage includes improved car-following models [8,55], driver's lane-changing decision behavior [53], right-turn behaviors at intersections [32], motorcarpedestrian mixed traffic in share space [1], to the cyclist's conflict avoidance behaviors in mixed traffic at un-signalized intersections [25]. ...
Car-following (CF) and lane-changing (LC) behavior models have been widely studied separately as the core models of traffic simulation. However, in practice, CF and LC are inseparable and thus integrated driving (ID) models containing CF and LC behaviors emerge. Here, we proposed a new work to introduce the social force (SF) model to the operational ID behavioral model on the highway. First, a data-driven-based operational ID behavioral model is proposed in the hierarchical social force behavioral model framework. Then, the inputs/output of the SF-ID behavioral model is determined. SF-ID model is built by the feed-forward neural networks (FNN), and the network structure and other parameters are calibrated and verified by field data. Results of the test on CF and LC situations show that our proposed FNN SF-ID model has a good capability in reproducing/predicting the operational ID behaviors on the highway. In addition, we also analyzed the structural features of the FNN SF-ID models, and refine the original models by removing insignificant inputs. The comparison results showed that the refined model-FNN SF-ID (R)-performed better than the original models.
... Notably, the spring forces are similar to what several authors refer to as social force (Helbing & Tilch, 1998;Lin, Ma, Li, & Wang, 2016). The stiffness of the multispring 1 and the spring oscillator 2 represents the different motivations of the driver, which an individual experiences in each vehicle. ...
Intelligent‐driving technologies play crucial roles in reducing road‐traffic accidents and ensuring more convenience while driving. One of the significant challenges in developing an intelligent vehicle is how to operate it safely without causing fear in other human drivers. This paper presents a new behavioral decision‐making model to achieve both safety and high efficiency and also to reduce the adverse effect of autonomous vehicles on the other road users while driving. Moreover, we attempt to adapt the model for human drivers so that users can understand, adapt, and utilize intelligent‐driving technologies. Furthermore, this paper proposes a combined spring model for assessing driving risk. Thus, we analyze some driving characteristics of drivers and choose “safety” and “high efficiency” as the two main factors that are pursued by drivers while driving. Based on the principle of least action, a multiobjective optimization cost function is established for the decision‐making model. Finally, we design six unprotected left‐turn scenarios at a T‐intersection and three unprotected left‐turn scenarios at a standard two‐lane intersection for applying simulation algorithm and provide a decision‐making map for developing intelligent‐driving technologies. Based on the principle of least action, this paper demonstrates that optimization theory can give insight into drivers’ behavior and can also contribute to the development of intelligent‐driving algorithms. The experimental results reveal that the behavioral decision‐making model can always avoid collision accidents on the premise of ensuring certain efficiency, and it can achieve 97.01%, 94.52%, 96.67%, 91.18%, 101.27%, 83.33%, 102.94%, 103.03%, and 105.77% of time to intersection's maximum pass rate in the considered nine scenarios.
... According to traffic laws, vehicular traffic turning left and facing a permitted phase must yield the right-of-way to oncoming traffic; however, this situation is not the case in some countries, including China, Norway, and Finland. Drivers in these countries may not follow the full compliance with the official priority rules and instead fail to yield [1][2][3]. Leftturning vehicles always attempt to choose the shortest path where the potential conflict point is nearer to themselves than through-vehicles. Once an opposing through-vehicle accommodates a left-turning vehicle's crossing, the following left-turning vehicles will take this opportunity to finish their turning movements. ...
A new method has been developed for estimating the capacity of an exclusive left lane with a permitted phase under nonstrict priority. Different from maneuvers under strict priority, these left-turning vehicles were released in the form of a left-turn group. A field survey was first conducted to explore the maximum number of vehicles in a left-turn group, and the releasing process of the permitted left turns. The observations revealed that (1) the maximum number is related to the intersection geometry and (2) the releasing process includes two stages: the first left-turn group crossing at the beginning of a permitted phase and the following left-turn groups crossing using gaps provided by opposing right turns. Next, a method based on probability theory and these observation results were applied to estimate the capacity of an exclusive left lane. The procedure contains two stages and eight steps. Finally, the estimation of the left-turn capacity using the proposed model was validated by comparing the capacity from the strict priority and actual maximum volumes.
