Stop Line Research Articles

Strategies for unplanned gait termination at comfortable and fast walking speeds in children with cerebral palsy

Collision avoidance while walking is necessary for safe living, and faster walking speeds tend to increase collision risk. However, gait termination strategies for patients with cerebral palsy (CP), from comfortable to faster speed, remain unexplored. This study aimed to analyze these strategies in children with CP compared to typically developing (TD) children at two different speeds. Study participants included 10 children with CP (mean age, 12.5; five females; mean height, 147.8 cm; mean weight, 41.7 kg) and 10 TD children (mean age, 11.4; nine females; mean height, 142.0 cm; mean weight, 38.1 kg). Effects of walking speed on spatial, force, and temporal parameters were assessed at 100 % (WS1) and 125 % (WS2) speeds of comfortable walking. The TD group exerted a more pronounced braking force at the first step after the stop line appeared on the floor until the contralateral step at both WS1 (P = 0.006) and WS2 (P = 0.019); however, the CP group exerted a more potent force after the second step (WS1: P = 0.026, WS2: P = 0.023) in the anterior-posterior (AP) direction. Additionally, an increase in the center of mass (COM)-center of pressure (COP) divergence in the AP direction (P = 0.032), which decreased in the mediolateral (ML) direction (P = 0.036) at faster walking speeds, influenced the kinetic characteristics of the CP group from WS1 to WS2. The complex adaptations, such as unique braking forces and changes in the COM-COP divergence, suggest that gait interventions should consider the distinctive forces and adopt dynamic balancing strategies to avoid collisions during walking.

Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

Impact of countdown timer on drivers’ anticipation at the onset of yellow at signalized intersections

ABSTRACT Countdown timers at traffic signals display the time remaining for a phase to terminate, influencing driver behaviour. Studies on their effects at the onset of yellow have generally focused on drivers’ stop/go decisions and dilemma zone, without explicitly addressing how they help drivers anticipate the signal change and make ‘informed decisions’. Therefore, this study first analysed the impact of countdown timer on drivers’ anticipation by comparing brake initiation, deceleration, and speed in the absence and presence of countdown timer. Next, driver behaviour was studied using ‘Accelerated Failure Time’ models. A driving simulator experiment was designed for data collection, with demographic and additional driving behaviour data gathered through a questionnaire. When countdown timer was present, drivers anticipated the onset of yellow, resulting in significant variations in driver behaviour. These variations, in turn, influenced the duration of braking and the time taken to cross the stop line from the onset of yellow.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Extending the decision-making process during yellow phase from human drivers to autonomous vehicles: A microsimulation study with safety considerations

One of the main factors affecting the safety of signalised intersections is the stop/go behaviour during the yellow interval. Although previous research has exhaustively examined drivers' stop/go decision-making, the expected autonomous vehicles' (AVs') stop/go behaviour has not yet been thoroughly investigated. Through a series of simulation experiments developed for conventional and autonomous vehicles using different car-following, lane-changing, lateral placement and stop/go model parameter values, we examine here whether the default VISSIM stop/go parameter values can adequately replicate the observed drivers' behaviour at the considered intersection and assess the suitability of using the currently available options, albeit referring to human drivers, to simulate the expected stop/go behaviour of AVs. We also propose a policy framework for determining the desired behaviour of AVs in yellow interval, which is integrated into an AVs logic and achieved in the last simulation to explore the effect of automation on the stop/go outcome and, hence, on the safety level of signalised intersections. Several data analysis and modeling techniques were used for the formulation of certain scenarios, including binary choice models. The default stop/go parameter values were found unfit to replicate the observed stop/go behaviour and subjected to calibration. Compared to the currently available options, the proposed AVs logic proved to produce the most accurate results, in terms of the stop/go simulation outcome. Regarding the impact of automation on the stop/go outcome, the simulation experiments showed that AVs preferred a more conservative behaviour in favor of road safety, as indicated by the significant reduction (≈15%) in the number of vehicles crossing the stop line during the yellow light and zero instances of red light violation. However, compared to the conservative drivers represented by the default stop/go parameter values, AVs preferred a more rational behaviour in favor of intersection capacity without compromising road safety.

Multi-Vehicle Collaborative Trajectory Planning for Emergency Vehicle Priority at Autonomous Intersections

Emergency vehicle (EV) prioritization plays an important role in improving rescue efficiency and saving lives and property. Most existing studies have been confined to either investigating signal prioritization for EVs at intersections or focusing on clearing emergency lanes on road segments, with limited consideration for an integrated approach that combines both methods. In this study, we have developed a bi-level trajectory planning model aimed at optimizing the trajectories of EVs at intersections and road segments located within the communication range, using vehicle-to-everything technology. The upper-level model is designed to plan the entry times, speeds, and internal trajectories of vehicles within the intersection while avoiding any conflicts that may arise during their movements. In particular, to address the dimensionality of the proposed large-scale optimization problems, we introduce the equidistant discretization method to discretize the entry speeds of vehicles into a finite set of selectable values. The lower-level model focuses on optimizing the longitudinal and lateral trajectories on roadways to ensure that vehicles arrive at the stop line punctually and promptly. Both the upper- and lower-level models are formulated as mixed-integer linear programming models. The A Mathematical Programming Language and the Gurobi solver are employed for optimization. The case study demonstrates that the proposed model effectively optimizes vehicle trajectories at intersections and road segments, leading to a significant reduction in delays for EVs.

Research Progress and Prospects of Transit Priority Signal Intersection Control Considering Carbon Emissions in a Connected Vehicle Environment

Transit priority control is not only an important means for improving the operating speed and reliability of public transport systems, but it is also a key measure for promoting green and sustainable urban transportation development. A review of signal intersection transit priority control strategy in a connected vehicle environment is conducive to discovering important research results on transit priority control at home and abroad and will promote further developments in urban public transport. This study analyzed and reviewed signal intersection transit priority control at four levels: traffic control sub-area divisions, transit signal priority (TSP) strategy, speed guidance strategy, and the impacts of intersection signal control on carbon emissions. In summary, the findings were the following: (1) In traffic control sub-area divisions, the existing methods were mainly based on the similarity of traffic characteristics and used clustering or search methods to divide the intersections with high similarity into the same control sub-areas. (2) The existing studies on the TSP control strategy have mainly focused on transit priority control based on fixed phase sequences or phase combinations under the condition of exclusive bus lanes. (3) Studies on speed guidance strategy were mainly based on using constant bus speeds to predict bus arrival times at intersection stop lines, and it was common to guide only based on bus speed. (4) The carbon emissions model for vehicles within the intersection mainly considered two types of vehicles, namely, fuel vehicles and pure electric vehicles. Finally, by analyzing deficiencies in the existing studies, future development directions for transit priority control are proposed.

Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach.

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

The location analysis and efficient control of hidden coal spontaneous combustion disaster in coal mine goaf: A case study

Coal spontaneous combustion significantly threatens the mine production safety. This paper focuses on the practical aspects of the disaster within the sealed goaf of Pingmei No. 11 mine. Obtained the characteristic parameters of spontaneous combustion in goaf by arranging high-level drilling field and drilling method. The grey correlation analysis method was applied to analyze the determination index of the effective characterization of the hidden spontaneous combustion area range, and interpolation aided in assessing these indicators before and after the increase amount of N2 injection. All showed that the hidden spontaneous combustion area was determined to be 70–80 m away from the return air side and 10–15 m away from the stop line, and the comprehensive treatment technologies are executed to control the spontaneous combustion hazards, including injecting inert gas to cool the high-temperature area, injecting the aqueous foam to cover the fire source, and plugging leakage to isolation the O2. Finally, the O2 concentration in goaf dropped below 5.0%, and carbon monoxide concentration fell below 0.001% after implementing these methods, effectively controlling the coal spontaneous combustion disaster. The successful management of concealed coal spontaneous combustion disasters offers a valuable reference for identifying and efficiently addressing such incidents in goaf areas.

Comparative and Microscopic Analysis of Vehicle Startup Behavior in Red Countdown Traffic Lights with Various Display Forms

Background: Countdown-type traffic lights provide drivers with the time remaining before the signal turns green or red. Most countdown devices inform the drivers of the exact signal switching time numerically on a display form. This study focuses on two vehicle startup behaviors when a red signal phase countdown (RC) is displayed: a premature start (PS) and an early reaction (ER). The PS is a vehicle crossing the stop line before the green phase, and the ER is a vehicle starting to move before the green phase. While there are many studies on PS rates under RC, there are only a few studies on ER rates. There are also few studies that have analyzed the relationship between the PS and ER rates and the display forms of RC have also been analyzed in terms of micro-behavior. Objective: This study microscopically analyzes the ER and PS by including data within 1 second at signal switching while comparing 10 types of display forms in RC in order to obtain the ideas of safer and more efficient display forms in signal countdown. Methods: We conducted the experiments with RC traffic lights of different display forms, and the surveys of no countdown (NC). Results: From the very limited conditions of the experiment, we found that there is a threshold at which the ER rates in RC and NC differ significantly. Conclusion: We were also able to analyze the basic characteristics related to the display forms of RC, which is a quick start and which reduces PS and ER.

Analysis of The Psychological State of Vehicle Drivers at The Intersection at The End of The Green Light and The Impact of Their Behavior

Driving safety is a core area of road research, and the casualties and property damage caused by traffic accidents are enormous. Since most accidents are related to drivers, studying their behavior and psychology is essential to improve driving safety. This paper mainly focuses on the driver’s psychological state at the end of the green light of the traffic light (countdown 3s), and the behavior and detention of the driver at the end of the green light. Through psychological investigation, drivers who choose to pass the stop line at the end of the countdown to the green light can be divided into three types: adventurous, cautious, and peaceful. Comparing the situation of the on-site investigation with the psychological investigation, it is found that the driver’s judgment of the driving process has a certain error with the real situation.

Motorcyclists’ Stopping Position at 4-Legged Signalised Intersections

Malaysia was ranked seventh in the world for having the highest percentage of motorcycle fatalities. The majority of motorcycle fatalities occurred at intersections as a result of other motor vehicles failing to notice them. However, little is known about the behaviour of motorcyclists when they come to a stop at an intersection. This study was designed to examine the position of motorcycles during the red phase at 4-legged signalised intersections. Data from 4,133 motorcycles were collected at four locations in peninsular Malaysia throughout peak and off-peak hours. Motorcycles stopped at signalised intersections by stopping behind the stop line, at/in front of the stop line, at the yellow box, at the pedestrian crossing and at the island marking were collected through video recording. Therefore, to facilitate the analysis, motorcycle positions were classified as either stopping behind the stop line or stopping forward. As per findings, more than 63 percent of motorcyclist’s queue in front of the stop line and move forward during peak and off-peak hours to gain a clear view, which increases the likelihood of a motorcycle departing within a very short time during the initial period of the green signal. To improve the performance of signalised intersections, some traffic engineering approaches can be used, such as the provision of a dedicated area, known as a motorcycle red box, which separates motorcycles from other types of vehicles at traffic lights. Positioning motorcycles ahead of other drivers reduces the risk of motorcycles appearing out of nowhere when traffic begins. This segregation is expected to improve motorcyclist visibility and safety, as well as provide a buffer for pedestrian crossings at signalised intersections, providing better protection for vulnerable road users.

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.

CROSSING SPEEDS OF CARS THROUGH SIGNALIZED INTERSECTIONS

The article deals with the problem of traffic in three traffic-light intersections in the street network of Iasi city. Usually, the existing traffic lights do not fully take into account the size, structure and parameters of the queue of vehicles waiting at intersections; which in turn affects the traffic capacity of the intersection. In the study, we used a representative set of measurements taken at the three intersections to interpret the variation in the crossing speeds of the traffic light intersection space by the vehicle columns using the linear regression method. We studied several categories of vehicles queuing before the stop line at the intersection. We made a statistical analysis of the time taken to cross three intersections by vehicles in different starting positions. The main results of the research showed estimates in good agreement with observational data as well as a significant reduction in vehicle speed (to 58%) when there are different categories of vehicles queuing at the intersection. This study can be used in the urban traffic diagnosis method, being part of a traffic study together with urban traffic forecasting and therapy.

Experimental investigation on stress distribution and migration of the overburden during the mining process in deep coal seam mining

Coal mining has a significant impact on the movement of the overburden, leading to potential safety hazards in the working face. In this paper, a similarity simulation experiment was conducted to investigate the migration of overburden during the mining process of a specific working face in the Liuzhuang Coal Mine located in southern China. Sand and gravel were used to simulate the geological environment of each rock stratum. The deformation of the stratum was monitored using strain gauges, the fracture and displacement changes of the overburden stratum were recorded using cameras, and the characteristics of roof collapse was monitored using infrared thermal imager. The experimental model fully simulated the situation of the working face, and the actual working face size was obtained by enlarging the model by 100 times. The experiment found that during the initial stage of mining, there was no significant subsidence of the roof. In the course of the advancement of the working face, the primary roof intermittently fractured behind the working face, with subsequent propagation of upper cracks in an upward direction. The overburden rock layer above the goaf experienced continuous compaction, leading to the gradual closure of the separation layer. Simultaneously, new cracks constantly emerged in front of the working face, resulting in the progressive stabilization of the height of the crack zone. The stress measurements at each point exhibit a pattern of initial increased, followed by decrease, and ultimately stabilization. By considering the stress variation law of the overburden rock, the stress changes in key layers of the bedrock during mining could be categorized into four zones: the stress stable zone, stress increasing zone, stress reducing zone, and compaction stable zone. During the initial phase of coal seam mining, the presence of rock layers provided support, resulting in minimal subsidence of the overburden rock. However, as the mining operation progressed, the disturbance force and collapse of the overburden rock leaded to further downward subsidence. When the working face reached the stop line, the collapsed overburden rock gradually consolidates, resulting in a deceleration of energy release and the formation of a pressure relief zone. Consequently, the overburden rock above the working face underwent a slight additional subsidence, reaching its maximum level. A short cantilever rock beam structure was formed in the experiment. This study will provide valuable reference for future coal mining and serve as a vital theoretical basis for roof control in deep coal seam mining.

Mitigation of signalized intersection collision risks with trajectory based dynamic dilemma zone protection

Dilemma zone is one of the major factors causing red-light violations, right-angled and rear-end crashes at signalized intersections. In this paper, a dilemma zone protection system is introduced, which employs a dynamic vehicular trajectory optimization approach to guide vehicles approaching a signalized intersection. Unlike conventional methods that aim to eliminate dilemma zones, this system adjusts the speed profiles of individual vehicles to shift the distribution of dilemma zones and prevent vehicles from becoming trapped. Extensive simulated experiments were conducted to test and validate the proposed system for both individual vehicles and platoons. Results demonstrate that the system offers superior protection for individual vehicles, with full coverage across various settings of initial speeds and distances to the stop line. In the traffic environment with realistic platooning settings, the proposed system significantly reduces the number of vehicles in the dilemma zone, resulting in improved operational and safety benefits such as reduced risks of hazardous maneuvers and savings in vehicular delay.

Negative Impact of Traffic Congestion on Air Pollution and Estimation Delay Mode for Signalized Intersections in Babil City Utilizing SIDRA8.0 Plus Software

Traffic congestion is one of the most important factors contributing to air pollution. Vehicle emissions account for a significant portion of this pollution. Estimate the negative impact of traffic congestion by empirical model for predicting total delay time as a function of traffic conditions and geometric features and calculating the quantities of gases emitted during the traffic congestion at the signalled intersection is the goal of this paper. Two signalized intersections in Babil City with different characteristics are select. video recording is used to measure traffic volumes during peak and off-peak times. Afield survey is used to measure geometric design elements, signal timings, phasing, and selected emissions of HC, CO, and CO2 in (kg/hr) are based on delay. with 95% confidence, a linear regression model predicted the delay model. according to the statistical analysis, both geometry and traffic characteristics have a substantial influence on delay time. The constructed regression model for estimating delay has a high degree of correlation with actual values. The proportion of the total width of the exit route of an intersection of straight-forward departing traffic to the total width of groups of lanes departing on the same exit route at the same phase at the stop line it is observed that although the SIDRA 8.0 PLUS delay overestimates the delay at the high (v/c) range, there is no noticeable difference with the field delay and the study will offer suggestions to transportation and public health officials on how to try eco-routing and deco-signal timing to lower the risk of air pollutants.

RISKY BEHAVIOURS AMONG DELIVERY RIDERS AT SIGNALIZED INTERSECTIONS IN MALAYSIA

The increasing number of food delivery riders, known as P-hailing riders, has increased the number of traffic crashes involving this group. The high demand for online orders and delivery time constraints causes the delivery riders to engage in risky riding behaviours during delivery. Thus, there is a need to study this alarming problem before it becomes more serious. This study aims to determine factors influencing risky behaviours among delivery riders at signalized intersections. A roadside observation was conducted in the month of October 2022 at Subang Jaya, Selangor. A total of 19,803 delivery riders were observed for four days observations at two signalized T- and two cross-signalized intersections. A bivariate analysis (logistic regression) was applied to determine the relationship between seven risky behaviours (unfastened helmet, not wearing shoes, incomplete set of side mirrors, red light running, mobile phone use, stopping after stop lines, and abrupt lane change) with five explanatory variables including the day of the week, time of the day, weather condition, type of intersection and approach road. The result shows that red light running, and mobile phone use is more likely to occur during the weekend. Clear weather increases risky behaviours such as red light running, using a mobile phone, and abrupt lane changes. The findings of this study are useful as input to the related authorities in developing programs to decrease the number of crashes involving food delivery riders.

Analyzing Takeaway E-Bikers’ Risky Riding Behaviors and Formation Mechanism at Urban Intersections with the Structural Equation Model

To study the internal formation mechanisms of risky riding behaviors of takeaway e-bikers at urban intersections, we designed a takeaway riding risky behavior questionnaire and obtained 605 valid samples. An exploratory factor analysis was then conducted to extract five scales: individual characteristics, safety attitude, riding confidence, risk perception, and risky riding behavior. On this basis, a structural equation model was constructed to explore the intrinsic causal relationships among the variables that affect the risky riding behaviors of takeaway e-bikers. The results show that the influence of incentive compensation driven by the takeaway platform was the greatest one. Takeaway riders tend to fight against time to improve punctuality and income by red-light running and speeding. They usually need to pay attention to order information and the delivery routes and communicate with customers to pick up meals in real-time, which inevitably lead to the use of cell phone while riding. Road factors such as “no turnaround at the intersection” and “no non-isolation facilities between on-motorized and motorized lane” lead riders to riding against the traffic, riding on the motor lane, and parking outside the stop line. In addition, lax traffic regulations lead to frequent loopholes for takeaway riders. It means that improving the takeaway platform system, strengthening traffic safety education, and adopting mandatory restraint measures are extremely important. The empirical results provide theoretical support for the benign and healthy development of the takeaway industry, which is significant for preventing and reducing risky behaviors of takeaway riders and improving safety at urban intersections.