Comparative Study of Pedestrian Crossing Behaviour at Uncontrolled Intersection and Midblock Locations

User-satisfaction based operational evaluation for uncontrolled intersections under mixed traffic

Roundabout Capacity in Heterogeneous Traffic Condition: Modification of HCM Equation and Calibration

This paper focuses on the effects of mixed traffic circumstances of uncontrolled or unsignalized intersection under heavy flow when traffic is heterogeneous in character. On the basis of field data collected on Fuljhore road, Durgapur, it is clear that capacity reduces if the proportion of slower vehicles increases in the traffic stream. On the basis of field data collected, the present study also made it evident that the speed data deviate from the normal distribution curve and skewness of speed data is significantly high under the high density of traffic flow. The present study, therefore, examined the distribution of these data and concluded that their logarithmic transformation reasonably satisfies the normality assumption while modeling in terms of goodness-of-fit. Modeling traffic flow through urban uncontrolled intersections in developing countries like India is a complex process because of the high-level heterogeneity of traffic and absence of major and minor road concepts in traffic regulation schemes. Moreover, Indian codes have not been provided any relation which can be used to directly determine the delay at uncontrolled intersections under Indian traffic conditions. Uncontrolled intersections are vital points on urban roads, the performance of which will influence the flow of traffic on the road network.

Operational Evaluation of Uncontrolled Intersections using Artificial Intelligence Techniques

In the developing world, with increases in population, the number of vehicles is increasing tremendously. Traffic safety on roads has become a major concern even with advancements in technology and infrastructure. Traffic safety assessments and accident prediction based on accident data is a reactive approach. There are known drawbacks related to the reliability of accident data, especially in developing countries with large populations, such as India. It is, however, unethical to wait for accidents to occur before drawing statistically accurate conclusions regarding safety impacts. To overcome this impediment, one needs to develop accurate models that rely on surrogate safety measures (SSMs) for effective safety evaluations. The main advantage associated with the use of these models is that they can model crashes more frequently than in the real world and thereby imply an efficient and more statistically reliable proximal measure of traffic safety. The objective of this study is to examine the impact of management measures on traffic safety at a three-arm uncontrolled intersection with the use of microsimulation modeling under mixed traffic conditions. This examination was done by developing a unique methodology of measuring one SSM, postencroachment time (PET). This paper describes improvement in the accuracy of crash predictions by proposing a methodology to calculate PET.

Impacts of bus holding strategies on the performance of mixed traffic system

This research aims at developing a system of real-time simulation-based dynamic traffic model under mixed traffic flow conditions. The system consists of two layers, namely, simulation layer and real-time control layer. The system is implemented based on rolling horizon approach, which is advanced for each stage; thus real-time data can be incorporated within the framework. In order to predict normative, as well as predictive, information, a simulation-based dynamic traffic assignment model is employed within each stage. Empirical data for a real city network, such as flows from vehicle detectors, are used to validate the model in a real-time environment. The values of mean absolute percentage error and root-mean-squared percentage error are within 15%, and the results show promising agreements between observed and simulated flows.

Pedestrian crossings are important for accessing the adjacent land use facilities in daily life. Improperly designed crossing facilities cause discomfort to pedestrians and may even jeopardize their safety as well as being a hindrance to the smooth flow of vehicle traffic. These problems exist especially in developing countries such as India, where mixed traffic conditions prevail and road discipline is essentially absent. Thus, the design of efficient crossing facilities considering the flow of both pedestrians and vehicles is of utmost importance in these countries. In this regard, the objective of the current study was to propose warrants for pedestrian crossing facilities in midblock sections under mixed traffic conditions. To achieve this objective, pedestrian–vehicle ( PV2) conflict analysis was carried out at eight different unprotected midblock crosswalks in Mumbai, India. Videographic surveys were conducted and vehicle and pedestrian volumes were extracted, along with pedestrian-crossing characteristics such as crossing difficulty, safety and traffic, and vehicle characteristics. The initial computed PV2-values were adjusted by different factors, and k-means cluster analysis was carried out to determine the threshold PV2-values for selection of a suitable crosswalk facility. The study also highlighted the importance of taking into consideration site characteristics for evaluation of a midblock crosswalk facility. The results of this study are expected to have a wide range of applications in the design of new crosswalk facilities or in the improvement of existing pedestrian crosswalk facilities under mixed traffic conditions.

In recent years, substantial global efforts have promoted walking as a mode of active transportation because of its health benefits, environmental friendliness, and ability to reduce traffic congestion, among other advantages. However, increased walking also raises the risk of pedestrian injuries through greater exposure to traffic. While numerous studies have investigated the factors influencing pedestrian crash severity in developed countries, comprehensive analyses focusing on low- and middle-income countries remain limited. This study investigated factors influencing pedestrian-injury severity in the Republic of Serbia, using multinomial logistic regression analysis on crash data from 2016 to 2020. Key findings revealed that injury severity is significantly influenced by pedestrian age and gender, crash location, vehicle type, weather and surface conditions, time of day, and land use. Pedestrians over 65, incidents during morning and night periods, crashes involving trucks or buses, collisions on national roads, female pedestrians, and midblock locations are notably associated with higher odds of fatal and serious injuries. To enhance pedestrian safety, the study suggests implementing educational measures targeting older adults and women, focusing on safe road-crossing practices and the use of fluorescent clothing. Engineering interventions should include protective barriers, speed limits, improved lighting, and marked pedestrian crossings at midblock locations. Increased enforcement is recommended during high-risk times and locations, alongside better regulation of truck movements in pedestrian-heavy areas. Emergency medical services improvements are essential for ensuring rapid response to pedestrian crashes. This study underscores the need for advanced models and comprehensive data to better understand pedestrian-injury factors.

The role of the street environment in the way people cross roads in urban settings is modeled. Respondents were placed in real traffic conditions at the curbside of street blocks in the Tampa Bay, Florida, area for 3-min observations of the street environments. Without crossing the blocks, respondents stated their crossing preference at each of six blocks. The origin and destination of each crossing were hypothetically set and varied across the blocks. So were the options available: two options for crossing at an intersection and up to four options for crossing at midblock locations. Within the framework of discrete choice models, the stated preferences are explained with the street environment, including traffic conditions, roadway characteristics, and signal-control characteristics. All three components of the street environment are considered: midblock locations, intersections, and roadside environment. The data are described; a nested logit model of pedestrian street-crossing behavior is estimated; and its implications to researchers and practitioners are discussed.

For signalized locations, delay is one of the key parameters in assessing the level of service and redesigning the traffic signals. Around the world, Highway Capacity Manual (HCM) and Webster delay models are commonly used in calculating the vehicle delay at signalized intersections, notwithstanding that these models assume homogeneous traffic conditions and lane discipline. Furthermore, it is worth noting that previous studies have mostly focused on developing delay models for signalized intersections, whereas signalized midblock crosswalks were overlooked by past researchers. In view of this, the current study presents a modified vehicle delay model for signalized midblock crosswalks (SMCs) under mixed traffic conditions. Mixed traffic conditions refers to diverse vehicles, complex interactions, and no lane discipline on the road—conditions which are usually noticed in developing nations. The HCM vehicle delay model is modified in the current study by incorporating factors related to mixed traffic conditions and SMCs. To test the accuracy of the proposed model, it was validated with the field delay using data collected at other SMCs in Hyderabad, India. Field delay was calculated by plotting the measured queue length against the signal cycle length. The performance metrics of root mean square error (RMSE), mean absolute percentage error, Nash–Sutcliffe efficiency, RMSE-observations standard deviation ratio, and percent bias were used to test the accuracy of the proposed model. Results revealed that the proposed model yielded the lowest error rates and the highest correlation coefficients between modeled and actual vehicle delay. The maximum error between the field delay and the modeled delay was found to be less than 5%.

The evaluation of the round trip time ( τ) still forms the basis for the design of elevator traffic systems. The elevator round trip time is the time taken for the elevator to complete a full cycle of the building, picking up passengers from their origin floors and dropping them off at their destination floors. It is assumed that the elevator car transports P passengers from their origins to their destinations during one round trip. By dividing the value of the round trip time by the target interval, the required number of elevators in the group can be found and fed into the overall traffic design. Traditionally, simplified formulae have been used to evaluate the value of the round trip time under a number of simplifying assumptions. This paper develops the formulae for the most general case of mixed traffic conditions, whereby every floor can be an occupant floor and an exit/entrance floor (i.e. every floor can have a percentage population, U( i), and a percentage arrival rate, Parr( i)). However, the formulae developed make a simplification by assuming a constant passenger arrival model, rather than the widely accepted Poisson passenger arrival model. The new developed set of formulae comprises three parts: the kinematic part ( τK), the door part ( τS) and the passenger transfer part ( τP). The kinematic part in turn comprises six components: the up journey ( UJ) time; the down journey ( DJ) time; the upper connecting journey ( UCJ) time; the lower connecting journey ( LCJ) time; the down return journey ( DRJ) time; and the up return journey ( URJ) time. The derivation process is accompanied by stepwise verification of all the different components of the round trip time using the Monte Carlo simulation (MCS) method. The results of the formulae match those from the MCS to less than 0.0025%. Practical application: Engineers usually design elevator traffic systems under up-peak traffic conditions, where only incoming traffic is assumed. It is sometimes useful to assess the design under a mixture of traffic conditions (e.g. lunchtime conditions). The formulae developed in this paper can thus be used to allow the designer to evaluate the round trip time under a mixture of traffic conditions. In practice, the formulae would not be evaluated by hand, but implemented as a software programme. Once the designer has evaluated the round trip time under the specified mix of traffic conditions (e.g. 40% incoming traffic; 40% outgoing traffic; 20% inter-floor traffic), then he/she can divide that number by the target interval to find the required number of elevators. This result can then be compared to the required number of elevators under up-peak conditions to assess the adequacy of the design for these mixed traffic conditions.

This study introduces a novel feature-based long short-term memory (LSTM) neural network designed to predict the severity of pedestrian–vehicle interactions at unsignalized intersections, aiming to enhance traffic safety significantly. The LSTM model dynamically integrates real-time data variables such as pedestrian and vehicle speeds, distances, and types, enabling it to accurately predict potential hazardous interactions in mixed traffic conditions. Utilizing a comprehensive data set of over 4,300 pedestrian–vehicle interactions, the interactions were meticulously categorized from “Safe Passage” to “Conflict” based on detailed behavioral analyses. Training and validation of the LSTM model demonstrated high accuracy and strong generalization capabilities. The model demonstrated strong predictive capabilities across varying levels of pedestrian–vehicle interaction severity. It performed well in identifying Safe Passage interactions, with high precision and a balanced trade-off between precision and recall for Critical Events. The model also excelled in predicting conflicts, showing robustness in distinguishing these critical interactions. Further evaluation showed significant improvement over baseline models and consistent performance across different urban environments. After iterative retraining with external data, the model’s overall performance improved significantly, reflecting its capacity to generalize about diverse traffic conditions. The successful deployment of this LSTM model marks a significant advancement in real-time traffic safety technology. Its ability to provide accurate predictions of interaction severity offers a proactive tool for providing real-time warnings to mitigate pedestrian–vehicle collisions, highlighting its potential for integration into traffic management and autonomous vehicle systems. This study underscores the model’s applicability across various traffic environments, making it a vital asset for urban safety enhancements.

Modeling Pedestrian Delay at Signalized Intersection Crosswalks Under Mixed Traffic Condition

Dynamic prediction of queues and delays at signalized intersections utilize data obtained from automated traffic sensors as inputs for Intelligent Transportation Systems (ITS) applications. Errors are inevitable during automated data handling, especially when the traffic involved is heterogeneous and lacking in lane discipline. This paper presents a model-based estimation scheme that can handle both mixed traffic conditions and erroneous detector input data to estimate queue and, in turn, delay. The models were developed for Queue within Advance Detector (QWAD) and Queue beyond Advance Detector (QBAD) scenario. The statistical properties of detector errors were incorporated into the estimation scheme, and the scheme was tested for varying traffic conditions. The estimation scheme’s performance was evaluated using field data from a signalized intersection in Chennai, India, and simulated data. It is found that the incorporation of statistical properties of detector error allows accurate estimation of queues and delays despite erroneous data input.