Pedestrian-vehicle Interactions Research Articles

A Coordinated Allocation Method for Right-Turn Strategy at Signalized Intersections with Optimal Pedestrian and Vehicle Delays

With strict enforcement of pedestrian right of way at all intersections, the inappropriate right-turn resource allocation from a spatial and temporal perspective will lead to a reduction in the operational efficiency of the intersection. In this paper, three spatiotemporal resource allocation schemes for right-turning vehicles are proposed, considering the vehicle and pedestrian traffic efficiency in all directions of the intersection. To minimize vehicle and pedestrian delay at the intersection individually, an optimization model is established with the effective green time of each phase and three schemes as decision variables. A right-turn vehicle and pedestrian conflict delay model is developed based on the pedestrian-vehicle interaction behavior as the constraints of the optimization model. The NSGA-II algorithm is used to solve the model, and the quantitative criteria for the exclusive right-turn lane and phase are obtained by sensitivity analysis. The results of this paper can be used as a guide for traffic design and for planning and controlling the operation of right-turning vehicles at intersections.

A Computer Vision-Based Algorithm for Detecting Vehicle Yielding to Pedestrians

Computer vision has made remarkable progress in traffic surveillance, but determining whether a motor vehicle yields to pedestrians still requires considerable human effort. This study proposes an automated method for detecting whether a vehicle yields to pedestrians in intelligent transportation systems. The method employs a target-tracking algorithm that uses feature maps and license plate IDs to track the motion of relevant elements in the camera’s field of view. By analyzing the positions of motor vehicles and pedestrians over time, we predict the warning points of pedestrians and hazardous areas in front of vehicles to determine whether the vehicles yield to pedestrians. Extensive experiments are conducted on the MOT16 dataset, real traffic street scene video dataset, and a Unity3D virtual simulation scene dataset combined with SUMO, which demonstrating the superiority of this tracking algorithms. Compared to the current state-of-the-art methods, this method demonstrates significant improvements in processing speed without compromising accuracy. Specifically, this approach substantially outperforms in operational efficiency, thus catering aptly to real-time recognition requirements. This meticulous experimentation and evaluations reveal a commendable reduction in ID switches, enhancing the reliability of violation attributions to the correct vehicles. Such enhancement is crucial in practical urban settings characterized by dynamic interactions and variable conditions. This approach can be applied in various weather, time, and road conditions, achieving high predictive accuracy and interpretability in detecting vehicle–pedestrian interactions. This advanced algorithm illuminates the viable pathways for integrating technological innovation and sustainability, paving the way for more resilient and intelligent urban ecosystems.

Resource Optimization for Task Offloading With Real-Time Location Prediction in Pedestrian-Vehicle Interaction Scenarios

With the development of autonomous driving, task offloading of Internet of vehicles has become a hot research issue. In pedestrian-vehicle interaction scenarios, characteristics of tasks are constantly changing due to the influence of pedestrians and road conditions. Real-time offloading optimization and signaling are time-consuming, which may not meet the low delay requirement of task offloading. Therefore, this paper proposes a location prediction-based resource optimization scheme for task offloading in these scenarios. Firstly, the locations of pedestrians are predicted by the social force model based on their movement rules, and the locations of vehicles are predicted by the car-following model on the basis of ensuring pedestrian safety. The characteristics of tasks are obtained based on the predicted locations of vehicles. Then a neural network trained beforehand based on deep Q-learning is used to obtain a task offloading strategy. Since the tasks are obtained by prediction in advance, this strategy decision can be processed before vehicles arriving the predicted locations, which saves the time consumption of optimization and signaling. Besides, simulation results show that the proposed scheme still guarantees an acceptably low task offloading delay compared with the other methods, especially in congested areas.

Development and validation of a video-based assessment tool for children's street-crossing safety

BackgroundThe alarming rate of child pedestrian fatalities highlights an urgent imperative for comprehensive test on children's street-crossing safety. Various methods in children's street-crossing studies were reviewed, and the advantages of the video-based experimental real-walk methods were introduced. AimThe present study aimed to develop a reliable assessment tool to evaluate children's crossing safety, assess its validity, and identify developmental characteristics of children's crossing abilities. MethodsWe recorded scenarios of vehicle-pedestrian interactions with varying distances and speeds at multiple suburban zebra crossings without traffic lights, and projected the videos to guide participants through a realistic crossing experience indoors. Sixty children, with 20 in each age group of 6, 8, and 11 years old (with equal numbers of boys and girls), were recruited. The children's crossing safety was measured using the dangerous crossing (DC), safe crossing rate (SCR) and time to contact (TTC), and they were also asked to complete the Chinese version of the Pedestrian Behavior Scale (CPBS). Their eye movements were also recorded with a portable eye tracker. ResultsThe correlation analysis of the simulated street-crossing video test revealed significant associations between behavioral indicators (DC, TTC), CPBS, and eye movement indicators, indicating strong empirical validity. Difficulty analysis confirmed the relatively high SCR among child pedestrians across all three age groups. Discriminant validity analysis demonstrated that age significantly predicted children's crossing safety, with 6-year-olds exhibiting lower safety in both behavioral and attentional dimensions compared to 8-year-olds and 11-year-olds. Finally, predictive validity results indicated varying effects of TTC on CPBS scores across different age groups, with a more significant predictive effect for younger children possessing lower street-crossing abilities. ConclusionThe video test effectively verified the critical period and developmental trend of children's crossing ability and demonstrated high validity. Overall, the developed video test is an effective tool for assessing school-aged children's crossing safety.

A comparison of pedestrian behavior in interactions with autonomous and human-driven vehicles: An extreme value theory approach

Autonomous Vehicle (AV) technologies are expected to result in significant safety and mobility benefits to the road system. However, one of the most important issues that autonomous vehicle technology faces is ensuring safe interactions with active road users such as pedestrians who can have unpredictable behavior. Moreover, road user behavior varies considerably across different traffic environments, which might represent a challenge to implementing AVs as they lack the intuition common in human-driven vehicles (HDV). This study proposes an approach to evaluate crash risk in vehicle–pedestrian interactions. An Extreme Value Theory (EVT) Peak Over Threshold (POT) model is used to compare the crash risk of AV-pedestrian and HDV-pedestrian interactions in four different cities, namely Boston, Las Vegas, Pittsburgh, and Singapore. A Bayesian hierarchical structure is used to incorporate the effect of several behavioral covariates, which enables estimating the crash risk of each interaction. Results show that the risk varies considerably depending on the type of interaction and the environment. For example, the impact of behavioral covariates (i.e., minimum distance between road users and maximum pedestrian speed) on the risk of AV-pedestrian interactions is greater when compared to the risk of HDV-pedestrian interactions in Boston, Las Vegas, and Singapore. This might indicate that, in busy and congested environments, road users may not be entirely comfortable with the presence of AVs. In addition, Singapore presented a higher percentage of riskier AV-pedestrian interactions when compared to the other cities. Finally, this study offers significant insights into the challenges of introducing AVs in diverse environments as behavior plays a crucial role in traffic and can influence conflict occurrence.

Pedestrian–vehicle interactions at unsignalized mid-block crosswalks: Effects on traffic flow, CO[formula omitted] emissions, and energy dissipation

Unsignalized mid-block crosswalks are adopted to regulate unauthorized crossings by promoting drivers’ yielding to pedestrians. Although pedestrian–vehicle interactions at crosswalks have been widely studied, the effects of such facilities regarding traffic flow and emissions have been overlooked. This paper presents a vehicle–pedestrian cellular automata model to analyze the effects of two types of crosswalk systems: raised and zebra, on traffic characteristics. The model allows microscopically studying the impact of the induced speed heterogeneity in terms of traffic flow, CO2 emissions, and kinetic energy dissipation. Simulation results show that traffic flow experiences four phases in both systems: maximum current, congestion, jamming, and blocking; with second-order transitions among them. The maximum current phase is the only one with free-flow conditions shrinking faster in the system with a raised crosswalk than in the one with zebra. Both systems reach the blocking phase when the pedestrian entrance rate surpasses a critical value. However, raised crosswalk has lesser repercussions in CO2 emission and energy dissipation by eliciting traffic calm. This study enhances the comprehension of the impacts of mid-block crosswalks, depending on the manner in which they are deployed.

Effect of vehicle external acceleration signal light on pedestrian-vehicle interaction

The number of casualties resulting from collisions between pedestrians and motor vehicles continues to rise. A significant factor is the misunderstanding of vehicle behavior intentions by pedestrians. This is especially true with the continuous development of vehicle automation technology, which has reduced direct interaction between drivers and the outside world. Therefore, accurate communication of vehicle behavior intentions is becoming increasingly important. The purpose of this study is to investigate the impact of external vehicle acceleration signal light on the interaction experience between pedestrians and vehicles. The differences between the use and nonuse of acceleration signal light are compared through controlled test track experiments in real scenarios and in videos.The results show that acceleration signal light help pedestrians understand vehicle behavior intentions more quickly and make safer crossing decisions as well as improving their perception of safety when crossing the street and their trust in vehicle behavior.

Interaction-Aware Decision-Making for Autonomous Vehicles

Complex, dynamic, and interactive environment brings huge challenges to autonomous driving technologies. Because of the strong interactions between different traffic participants, autonomous vehicles (AVs) must learn how to interact with other road users. Failure to consider interaction when making decisions may result in safety issues. In this paper, an interaction-aware decision-making approach is proposed for AVs. First, focusing on the interaction at uncontrolled mid-block crosswalks, the game theory is used to model the vehicle-pedestrian interaction (VPI). Then, an interaction inference framework is developed using the interaction model to obtain interaction information with pedestrians. Besides, a collaborative action planning method is proposed to generate collaborative actions. More importantly, interactive decision-making is formulated as an optimization problem by considering the task item and action item. Furthermore, considering pedestrians’ different levels of cooperation, the social force pedestrian model is developed. And then, a highly interactive environment is constructed. Finally, qualitative and quantitative evaluations are carried out against three baseline methods. The result shows that our method can interact with different pedestrians and balance safety and efficiency compared to baseline methods.

Pedestrian delay models for compliant & non-compliant behaviour at signalized midblock crosswalks under mixed traffic conditions

The present study aimed to propose new pedestrian delay models for Signalized Midblock Crosswalks (SMC) for mixed traffic conditions. A detailed study of the literature revealed that most of the existing pedestrian delay models were developed for signalized intersections. Thus, the need for the study was established and data were collected at eight SMC in Hyderabad, one of the most densely populated metropolitan cities in India, using video-graphic technique. Two delay models were developed based on the compliance behaviour and non-compliance behaviour of pedestrians. Both models have two components i.e., waiting delay and crossing delay where the latter has two subset components i.e., frictional delay and pedestrian-vehicle interaction delay. The bidirectional effect (pedestrian-pedestrian interaction while crossing the road) of pedestrians was addressed as frictional delay while the non-compliance behaviour by pedestrians and vehicles was addressed as pedestrian-vehicle interaction delay in the present models. The waiting delay component was defined by modifying the Webster delay model for non-uniform pedestrian arrivals. The proposed delay models yielded an error of 5% and 7% for compliance behaviour model and non-compliance behaviour model respectively. The proposed models can be used for optimizing the signal timings and defining Level of Service (LOS) of facilities.

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle-Pedestrian Interactions.

Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians' unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle-pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle-pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle-pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle-pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle-pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle-pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

Pedestrian Crossing Intention Prediction Model Considering Social Interaction between Multi-Pedestrians and Multi-Vehicles

Accurate pedestrian crossing intention prediction is critical for autonomous vehicles and advanced driver assistance systems. In multi-pedestrian and multi-vehicle interaction scenes, the social interaction between pedestrians and other traffic participants is ubiquitous, which affects pedestrian crossing decisions and the accuracy of prediction. However, previous studies on pedestrian crossing intention lack comprehensive consideration and mathematical modeling of the social interaction. We propose a “social interaction force” (SIF) to identify and quantify social interaction behaviors and combine the hidden Markov model (HMM) to predict pedestrian crossing intentions 1.0 s ahead. Firstly, a large dataset of pedestrian-vehicle interaction samples is collected from two views, and high-dimensional features are extracted for pedestrian intention prediction. Next, the concept of SIF is proposed to quantitatively characterize the influence of other pedestrians and vehicles on pedestrian crossing decisions, including “pedestrian interaction force” and “pedestrian-vehicle interaction force.” Finally, SIF, pedestrian features, and road structure features are input into HMM. Sliding time windows are applied to the HMM to achieve dynamic prediction of pedestrian intention sequences. Experimental results show that the recognition accuracy of the proposed model is 0.976, and the accuracy of 1.0 s ahead prediction is 0.932 with guaranteed prediction speed. The proposed model performance is superior to that of the most prevalent models developed thus far.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

A study on meta-analysis approach for pedestrian-vehicle interaction using LiDAR

To analyse the continuous and intricate interaction between pedestrians and vehicles and to safeguard them, high-resolution micro-traffic data is needed. In comparison to other traditional means of data collection, light detection and ranging (LiDAR) technology is gaining popularity since it can deliver this information with less computing effort and is weather-resistant. This article uses LiDAR technology to analyse qualitative and quantitative data related to pedestrian-vehicle interaction. Based on the study selection criteria, a total of 39 studies were included in the qualitative analysis. Based on the availability of the data, four of these studies were then included in the quantitative meta-analysis. Some of the algorithms developed thus far have a 99 percent accuracy rate. The analysis was carried out using the Fisher r-to-z transformed correlation coefficient as the outcome measure. The amount of heterogeneity was estimated using the restricted maximum-likelihood estimator. A random-effects model was fitted to the data. Q = 51.716, p = 0.0001, and I2 = 92.86 percent were the results of the Q test, demonstrating that there is enough heterogeneity amongst the studies. Neither the rank correlation nor the regression test indicated any funnel plot asymmetry. The findings indicate that LiDAR is a suitable technology for studying pedestrian-vehicle interaction. The study results are useful to explore the new technology of LiDAR and its suitability in pedestrian-vehicle interactions.

Evaluating Right-Turn Flashing Yellow Arrow for Vehicle–Pedestrian Interactions Using a Non-Probabilistic Regression Approach

Using video, interactions between right-turning vehicles and pedestrians were documented for sites with a permissive circular green or a flashing yellow arrow (FYA) permissive right-turn indication. Timestamps associated with key vehicle and pedestrian positions were documented and used to calculate three metrics: time for driver to complete right turn (with and without a pedestrian presence) and the time for the pedestrian to arrive at the theoretical conflict point of vehicle–pedestrian interactions. Using timestamp data, multiple non-probabilistic linear regression models were created to describe the relationship between the position of the pedestrian within the crosswalk and the time for a right-turning vehicle maneuver to be completed. Model results implicitly describe the attitude of drivers toward pedestrian presence on the crosswalk. Larger right-turning maneuver completion times are arguably an indicator of greater respect toward pedestrian presence. Given the nature of the model’s output, a pedestrian respect indicator (PRI) based on the value of model parameters is introduced as a potential indicator of the safety of vehicle–pedestrian interactions. PRI values can be computed for individual sites thus allowing its use as a safety indicator. The higher the PRI, the more “respect” toward pedestrians. Proof-of-concept modeling results based on available timestamp data suggest that interactions controlled by a right-turn FYA indication have a higher PRI than interactions controlled by a circular green. An argument is made that PRI values can be potentially used to proactively evaluate the effectiveness of traffic control devices and other safety countermeasures without the need for crash data.

Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR

In recent years, rapid advancements in the Autonomous Vehicles (AVs) industry have greatly motivated the research and development in pedestrian trajectory prediction and risk assessment. One of the critical requirements for AVs is to predict the future trajectories of pedestrians and provide collision warnings in an accurate and prompt manner. Nevertheless, accurate prediction of pedestrian trajectories remains a technical challenge, mainly caused by the heterogeneity of pedestrian crossing behavior and uncertainties in vehicle-pedestrian interactions. This paper proposes a deep learning-based method for pedestrian trajectory prediction and risk assessment, using trajectory data extracted from roadside LiDAR data and corresponding signal phasing information at MLK and Georgia Avenue in Chattanooga, TN. Meanwhile, a set of criteria referred to as the risk factor is established to quantitatively evaluate the risk of the pedestrian crossing behavior, which also serves as a learnable feature. A Long Short-Term Memory (LSTM) network is proposed, which takes the following data as the input: the pedestrian trajectory data, signal phasing data, and risk factors from the past 10 steps. Meanwhile, the network predicts the pedestrian trajectory and risk factor at the future time step. In the experimental study, the root-mean-square errors between the predicted and ground truth x and y coordinates are 0.225 meters and 0.377 meters, respectively, and the F1 score value for the risk factor is 99.6%, demonstrating the efficacy of the proposed LSTM-based methodology on pedestrian trajectory prediction and risk assessment.

Who goes first? A distributed simulator study of vehicle–pedestrian interaction

One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle–pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.

StreetAware: A High-Resolution Synchronized Multimodal Urban Scene Dataset.

Access to high-quality data is an important barrier in the digital analysis of urban settings, including applications within computer vision and urban design. Diverse forms of data collected from sensors in areas of high activity in the urban environment, particularly at street intersections, are valuable resources for researchers interpreting the dynamics between vehicles, pedestrians, and the built environment. In this paper, we present a high-resolution audio, video, and LiDAR dataset of three urban intersections in Brooklyn, New York, totaling almost 8 unique hours. The data were collected with custom Reconfigurable Environmental Intelligence Platform (REIP) sensors that were designed with the ability to accurately synchronize multiple video and audio inputs. The resulting data are novel in that they are inclusively multimodal, multi-angular, high-resolution, and synchronized. We demonstrate four ways the data could be utilized - (1) to discover and locate occluded objects using multiple sensors and modalities, (2) to associate audio events with their respective visual representations using both video and audio modes, (3) to track the amount of each type of object in a scene over time, and (4) to measure pedestrian speed using multiple synchronized camera views. In addition to these use cases, our data are available for other researchers to carry out analyses related to applying machine learning to understanding the urban environment (in which existing datasets may be inadequate), such as pedestrian-vehicle interaction modeling and pedestrian attribute recognition. Such analyses can help inform decisions made in the context of urban sensing and smart cities, including accessibility-aware urban design and Vision Zero initiatives.

Using Logistic Regression to Evaluate Pedestrian–Vehicle Interaction Severity at Side Street Green and Exclusive Phase Signals

This study used logistic regression to determine whether there is a significant difference in pedestrian–vehicle interaction severity at side street green and exclusive phase pedestrian signals, and to evaluate whether waiting- and crossing times are useful predictors of pedestrian–vehicle conflict. To do this, data related to the physical characteristics of each intersection and to the crossing experience of every pedestrian were gathered at 32 signalized intersections in Connecticut. At each intersection, conflicts between pedestrians and vehicles were classified into four distinct severity levels: undisturbed passage, potential conflict, minor conflict, and serious conflict. After interpreting the results of six different logistic regression refinements, it was determined that waiting time, crossing time, the number of lanes, annual average daily traffic, pedestrian compliance, phasing, and the presence of crosswalks were all useful predictors. Based on the results of this analysis, low wait time and crossing time values were associated with a decrease in the odds of a conflict. It was also determined that exclusive phase signals reduced the odds of a conflict by 85%. Future research should investigate variables associated with different land development patterns and demographic information. Along with this, the crossing experience of individuals who do not comply with pedestrian signals should also be evaluated.

Modified social force model for pedestrian–vehicle interactions at a signalized intersection

ABSTRACT Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase.

Identification of key determinants of personal safety perception at bus stops using proportional odds logistic regression

Complex vehicle-pedestrian interaction near the bus stop affects the safety perception of users, but the subjective nature of perceived safety and the lack of specific measurement techniques makes its estimation further laborious. This paper identifies factors influencing safety perceptions of bus users by employing Ordered Logistic Regression analysis. The study focussed on five dimensions; bus stops' design and surrounding facilities, traffic characteristics, and travellers' individual, travel and accident characteristics. The results demonstrated that increased vehicular flow, absence of footpath, crossing facility and safety barrier would scale down the perceived safety. Furthermore, the respondent's age, education, frequency of travelling by bus, familiarity with the bus stop, and previous accident experience are significant predictors of perceived safety. Results concluded that females perceived less safe, and no effects can be attributed to the household vehicles and income. Regarding road shoulder width, 1-2 meters is the most preferred width from the travellers' safety perspective.