Road Intersections Research Articles

GIS ANALYSIS OF NEIGHBOURHOOD ENVIRONMENT DETERMINANTS THAT INFLUENCE WALKABILITY IN PENANG, MALAYSIA

Understanding the relationship between urban design and physical activities is crucial. Studies have identified three key neighbourhood environment indices that enhance walkability. This research investigates the influences of these indices on residents’ walkability to community facilities in two neighbourhoods in Penang Island, Malaysia. Using geographic information system (GIS) technology, we analyse the effect of the built environment on walkability through buffering and thematic map analyses. Pulau Tikus features medium- and low-density housing with enriched community facilities, whilst Tanjung Tokong has a higher residential density, more road intersections and more diverse housing options. Findings indicate that Pulau Tikus offers better amenity accessibility, whilst Tanjung Tokong’s high-rise residential buildings and diverse housing types enhance walkability. This study highlights the relationship amongst residential density, road intersections, mixed land use and walkability in two residential neighbourhoods. Higher residential density and more road intersections increase walking activity, whilst mixed land use further influences resident mobility patterns. These insights can aid urban planners in designing more walkable, liveable and sustainable neighbourhoods.

Individual mobility prediction by considering current traveling features and historical activity chain

ABSTRACT Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.

Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city.

Navigation Path Prediction for Farmland Road Intersections Based on Improved Context Guided Network

Navigation Path Prediction for Farmland Road Intersections Based on Improved Context Guided Network

A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections

Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called Road Intersection Traffic Crash Prediction (RoadInTCP), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called topological-relational features (tr-features), of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to tr-features which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed RoadInTCP model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed RoadInTCP robustly outperforms existing methods.

AI-Based Adaptive Traffic Management

Traffic congestion is one of the biggest and most important issues facing modern city dwellers, necessitating effective and efficient solutions. AI-powered traffic light optimization has grown in popularity. The available literature indicates that intelligent approaches outperform traditional management techniques. The amount of cars on each intersection road determines the signal timings, which are adjusted to reduce traffic at the intersection. The recommended topology may significantly reduce traffic jams and wait times at the intersection. A lot of benefits can arise from an efficient traffic control system, such as less traffic on the roads, more economic efficiency, and enhancements in pedestrian safety and air quality. This paper discusses the several phases of the process, including data collecting, data analysis, and simulation development. At the conclusion of the process, a Python simulation model is created to test the topology.

Modeling the traffic accident probability of Urban roads based on an improved Bayesian network

ABSTRACT Urban road traffic accidents are severely threatening the safety of human life and property. In this study, the Random Forest (RF) algorithm was used to identify the significant risk factors of road section accidents and intersection accidents, and a probability prediction model of urban road traffic accidents based on the improved Bayesian network (IBN) was constructed. Next, a method is proposed to identify the accident-prone points on urban roads. The study results showed that: (1) There are significant differences in the factors influencing the probability of accidents at road sections and intersections. (2) When different combinations of influencing factors change, the probability of accidents at road sections and intersections also changes. Finally, based on the data from road sections and intersections in Tianjin, accident probability thresholds of 10.28% and 6.69% respectively have been determined, which can accurately identify the accident-prone points on urban roads.

Layout Planning of a Basic Public Transit Network Considering Expected Travel Times and Transportation Efficiency

Urban transit systems are crucial for modern cities, providing sustainable and efficient transportation solutions for residents’ daily commutes. Extensive research has been conducted on optimizing the design of transit systems. Among these studies, designing transit line trajectories and setting operating frequencies are critical components at the strategic planning level, and they are typically implemented in an urban integrated transportation network. However, its computational complexity grows exponentially with the expansion of urban integrated transportation networks, resulting in challenges to global optimization in large-scale cities. To address this problem, this study investigates the layout planning of a basic public transit network (BPTN) to simplify the urban integrated transportation network by filtering out road segments and intersections that are unattractive for both users and operators. A non-linear integer programming model is proposed to maximize the utility of the BPTN, which is defined as a weighted sum of expected travel times (from a user perspective) and transportation efficiency (from an operator perspective). An expected transit flow distribution (ETFD) analysis method is developed, combining different assignment approaches to evaluate the expected travel time and transportation efficiency of the BPTN under various types of transit systems. Moreover, we propose an objective–subjective integrated weighting approach to determine reasonable weight coefficients for travel time and transportation efficiency. The problem is solved by a heuristic solution framework with a topological graph simplification (TGS) process that further simplifies the BPTN into a small-scale graph. Numerical experiments demonstrate the efficacy of the proposed model and algorithm in achieving desirable BPTN layouts for different types of transit systems under variable demand structures. The scale of the BPTN is significantly reduced while maintaining a well-balanced trade-off between expected travel time and transportation efficiency.

Reconfigurable Intelligent Surface-Aided Security Enhancement for Vehicle-to-Vehicle Visible Light Communications

Vehicle-to-vehicle (V2V) visible light communication (VLC) systems are increasingly being deployed for real-time data exchange in intelligent transportation systems (ITS). However, these systems are highly vulnerable to eavesdropping, especially in scenarios such as road intersections where signals may be exposed to unauthorized receivers. To address these security challenges, we propose a novel reconfigurable intelligent surface (RIS)-assisted security enhancement scheme for V2V VLC networks. The proposed scheme leverages RIS to improve the reception of legitimate signals at the destination vehicle while simultaneously introducing artificial noise (AN) to interfere with potential eavesdroppers. Optimization problems are formulated to maximize the SINR of the destination vehicle and simultaneously minimize the worst-case SINR of eavesdroppers. The simulation results demonstrate that the proposed scheme achieves a notable improvement in the system’s secrecy rate by 1.64 bit/s/Hz and enhances the overall security performance, offering a robust solution to the security challenges in V2V VLC systems.

Rural parents’ route choices under different modes for school children drop-off and pick-up: a stated choice experiment

ABSTRACT Urbanization and school consolidation in rural areas have led to longer school commuting distances and changes in travel patterns, including increased use of private cars and e-bikes. As a result, greater parental involvement is needed to ensure safe and timely school commutes. This study explored how parents/guardians’ preferences for the built environment affect their route choices under different transportation modes in rural school commutes. Using a stated choice experiment and discrete choice models, the study identified key factors influencing route choices, such as travel distance and built environment characteristics like fewer road intersections, low traffic speeds, presence of sidewalks/bike lanes, road separation provisions, traffic lights, zebra crossings, green plants, and shops when walking, cycling, and riding an e-bike. The findings highlight the need to consider parents’ preferences when planning interventions and policies to enhance rural school travel, promote rural transportation and support diverse transportation modes in rural areas.

Design and Implementation of Device Circuit of Microcontroller-Based Smart Traffic Light Control System

In this paper, the design and implementation of a microcontroller-based smart traffic light control system is presented. The proposed smart traffic light is meant to mitigate or entirely eliminate hassles encountered by road users, mostly at road intersections. The system is sensitive to road density and therefore operates in smart and normal modes, with normal mode as the default; however, it switches to smart whenever the need arises. The approach adopted includes; typical intersection design, circuit diagram design, circuit analysis and overall system development and implementation. The microcontroller, which serves as the overall system brain is programmed using proteus electronic software; and then interfaced with already calibrated ultrasound modules and other components. Afterwards, it is initiated to confirm that research objectives have been met. The design employs a crossbar to prevent drivers from using a lane when not supposed. The traffic light control system, if implemented would significantly minimize traffic frustrations and ugly experiences road users encounter daily on Nigeria roads. However, this work is centred more on circuit operation and design, circuit analysis and overall system development and implementation.

Addressing Infrastructure Design Challenges In Constructing At-Grade Rail Crossings Near Road Intersections

At-grade rail crossings, where railway tracks intersect roadways at the same level rather than being separated by bridges or tunnels, are crucial elements of transportation infrastructure. However, these crossings create significant challenges for rail operations, particularly regarding safety and efficiency. Locating an at-grade rail crossing near a road intersection is generally discouraged due to the increased risk of train delays, reduced visibility for train operators, and the potential for train-road conflicts. While these issues are undesirable, they are sometimes unavoidable when rail infrastructure must be near or across road intersections. This study examines the design challenges of constructing at-grade rail crossings near road intersections. It provides a comprehensive review of the safety and operational concerns, including delays, conflicts, and the potential for accidents. The study also discusses existing guidelines and design practices to mitigate these challenges, offering insights into improving the efficiency and safety of at-grade rail crossings near road intersections.

Study About the Influence of the Road’s Angle Cut Slope in a “T” Intersection

Abstract The context in which this study is conducted is represented by the need to improve road safety at T-intersections, which can be visibility-critical locations and of the risk of accidents. Therefore, this study is important to identify and solve potential problems related to sight distance in road intersections. Case studies on sight distance in intersections are an effective way to assess the impact of the factors that influence it on road safety. Currently, the three-dimensional study allows us to analyze the visibility in detail according to the standards and obtain the sight distance both in tabular format and the graphic representation of the sight distance. The visibility triangle is defined as the spatial region formed by the path of the vehicle traveling along the main road and the vehicle traveling along the side road intersecting leading to the conflict point. The area of the visibility triangle depends on the prescribed sight distances, which must be observed based on the travel speeds of the conflicting paths. In this three-dimensional analysis, the sight distance at the intersection between a national road and an access road to a quarry, in the “T” variant, was studied. In this study, the sight distance checks were made in the “T” shaped intersection according to AND 600, with the help of the 3D model and the Civil Site Design software. Also, the influence of the angle cut slope on the sight distance from the intersections was studied considering 2 slope inclinations namely 1:2 and 1:4.

Lane extraction from trajectories at road intersections based on Graph Transformer Network

Lane-level road networks are crucial components of high-precision maps and play a significant role in intelligent transportation systems. Extracting lane-level road networks at intersections presents considerable challenges due to the complex structures of intersections and diverse driving behaviors. A graph-learning based method is proposed for extracting lanes from high-precision trajectories at road intersections. A trajectory relation graph is designed to encode the directional, shape, and distance features of trajectories, capturing both the intrinsic and extrinsic relationships between trajectories. Subsequently, a Graph Transformer Network is developed to extract a representative subset of trajectories as lanes. To alleviate the problem of generating missing and extraneous lanes, a set-based lane extraction loss is introduced to achieve implicit pruning of redundancy through the attention mechanism. Comprehensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods in three positional and topological accuracy metrics. The method achieves lane extraction with minimal omissions and redundancies and exhibits strong performance in complex scenarios such as U-turns, lane merging, and lane diverging regions.

A Snapshot of Factors Associated with the Severity of Crashes Involving Physically Impaired Drivers

Drivers with physical and/or mental impairments face many driving challenges. However, not many studies have been carried out to understand the factors that contribute to crashes involving these drivers and how these factors influence their crash outcomes. This study aims to address this gap in the road safety literature. The study uses historical crash data from the State of Alabama for at-fault physically impaired drivers and utilizes a random parameter with heterogeneity in a mean modeling approach to account for unobserved heterogeneity. The model estimation results reveal that in rural areas, driving over the speed limit, the time of crash being between 6.00 p.m. and 11.59 p.m., younger drivers, employed and distracted drivers were associated with severe injuries. Minor injury crashes are found to be associated with female drivers, state roads and residential areas. Finally, property-damage-only crashes are more associated with weekdays, driving under the influence of alcohol or drugs, a road with left curvature, driving too fast for the road conditions and intersections. The results obtained provide a foundation for the adoption of targeted countermeasures to improve highway safety for physically impaired drivers and all road users in general.

Assessment of Chemical Composition and Health Implications Associated with PM10 Exposure: A Comparative Study of an Urban Road Intersection and a Wood-Burning Vicinity

The chemical composition and potential health risks linked to human exposure to ambient PM10 particles were assessed at the Winneba highway intersection (WHI) and Apam, a wood-burning neighbourhood in Ghana’s Central Region. PM10 particulates were collected using a Gent sampler equipped with a Gast pump and a stacked filter unit. The sampling was done three times a week continuously from July 2022 to June 2023, for 24 h. A smoke stain reflectometer and an Ag-anode X-ray tube spectrometer were used to analyse the samples for black carbon and elemental contents, respectively. The health hazards associated with trace elements were evaluated using the US EPA health risk assessment model. The study’s findings indicated that the amounts of Cr (250.5 ng/m3) and Ni (358.1 ng/m3) detected at Apam surpassed the US EPA threshold limits, as did the values discovered at the WHI, which were 78.63 and 178.2 ng/m3, respectively. PCA indicated that PM10 particles were produced at WHI by soil dust, vehicle exhaust, brake and tyre wear, and two-stroke engines, whereas biomass burning, soil dust, and vehicle exhaust emissions contributed to PM10 at Apam. The non-carcinogenic risks of children’s exposure to trace metals in PM10 via the three exposure pathways were higher compared to adults. The overall cancer risk values for exposure to carcinogenic trace metals from ingestion and dermal contact were more than 10−4, suggesting high cancer risks: 9.49 × 10−4–5.20 × 10−3 for children and 9.03 × 10−4–5.26 × 10−3 for adults. Reducing PM10 and the health concerns it poses requires the use of clean fuel and the adoption of environmentally friendly transportation.

Experimental Study Unraveling Flow Allocation Patterns at Crossroad Intersections During Urban Flooding

Urban roads can effectively handle peak flows during extreme storms by serving as surface flood passages, provided the flow remains within safety thresholds for vehicles and pedestrians. However, studies on flow allocation at crossroad intersections are limited. Previous research has overlooked important factors: road transverse slope and turning radius. This study built a “two in, two out” laboratory crossroad intersection to examine flow allocation patterns. Experiments explored the effects of road longitudinal slope, boundary conditions, and the combined influence of turning radius and side slope. The results indicated that at flatter slopes, flow allocation is more influenced by road slope, while at steeper slopes, the inflow Froude number ratio becomes more significant. The combined effect of the turning radius and side slope results in a flow allocation that differs by 44.3% compared to rectangular orthogonal channel intersections. A straightforward formula is proposed to calculate the flow allocation ratio based on experimental results and inflow power ratio. These findings could improve road intersection designs for better flood mitigation, offering a practical tool for planning flood-resilient road networks.

Measurement and analysis of heterogeneous road transport parameters using Smart Traffic Analyzer and SUMO Simulator:An experimental approach

The main objective of research work is to study the characteristics of heterogeneous road transport environment & compare its dynamic parameters with the performance metrics measurement in terms of error rate & accuracy for vehicle count and classification using Smart Traffic Analyzer (STA) and SUMO Traffic Simulator. SUMO GUI is a simple tool for microscopic traffic simulation and helps to obtain vehicle dynamic parameters in the easiest way. Experimental research is also carried out by capturing live traffic video at study area of four way intersection road and analyzed through STA. The outcome results from SUMO and STA explicit overall accuracy of 96.63 %, and 95.62 % for vehicle count with the error rate of 3.35% & 4.37%. Similarly for vehicle classification, it provides the overall accuracy of 97.21% and 83.01% with the error rate of 2.78% & 16.97% respectively.

Conceptual Design of an Urban Pocket Park Located in the Site of the Occurrence of a Nineteenth-Century Chapel Using Representatives of Local Xerothermic Vegetation

The 19th century neo-Gothic chapel located in the Stradom district of Czestochowa, Southern Poland and built at the intersection of the main streets is a recognizable landmark of great historical value. Unfortunately, the current condition of the surroundings depreciates the charm of such a chapel, and thus does not encourage people to admire it. Therefore, in order to expose such a valuable object, we planned to create a conceptual design of a pocket park around this cultural monument. When choosing the location of any park, it is recommended to know the needs of the local residents, so we conducted a survey regarding their wishes. When designing the area, we intended to use species representing the vegetation characteristic of the Krakow–Czestochowa Upland located in Southern Poland. At the same time, we used a material typical of the area, namely limestone. Design principles around the chapel were taken into account, separating the sacred and profane zones with the intention of giving this site a unique character and creating the first urban pocket park of this kind.

Physiological changes in shrub species due to different sources of dust pollution in an urban environment.

Plants effectively filter ambient air by adsorbing particulate matter. The correct selection of landscape plants can exert greater dust retention benefits in different polluted areas. However, few studies have focused on the dust retention ability and related physiological responses of plants under continuous dust pollution from different dust sources. Here, we assessed the particle retention dynamics and plant physiology (chlorophyll content, soluble protein content, soluble sugar content, and peroxidase activity) of six shrubs (Berberis thunbergii var. atropurpurea, Ligustrum vicaryi, Rosa multiflora, Sorbaria sorbifolia, Swida alba, and Syzyga oblata) under continuous dust pollution from different dust sources (industrial sources: area below the direction of the coal-fired thermal power plant in Chengyang District, Qingdao, China; traffic sources: both sides of the road in each direction at the intersection of Great Wall Road and Zhengyang Road, Chengyang District, Qingdao, China; clean sources: Qingdao Agricultural University Campus, Qingdao Olympic Sculpture Park). The results showed that R. multiflora had the highest dust retention per unit leaf area of 3.27 ± 0.018g·m-2 and 2.886 ± 0.02g·m-2 in the experimental treatments of fuel source dust and clean source dust, respectively. The chlorophyll content of the tested shrubs significantly decreased due to the influence of dust treatment time, the range of cellular osmoregulatory substances (soluble sugars, soluble proteins, proline) tended to first increase and then decrease, and the antioxidant enzyme activities (superoxide dismutase, peroxidase) tended to increase and then decrease after continuous dust treatment. The greatest physiological changes were observed in plants within the industrial dust treatment area. The peroxidase activity and chlorophyll could be used as sensitive indicators of dust pollution in plants. R. multiflora showed better resistance to dust and had a greater dust retention capacity than other shrubs, making it more suitable for planting as a greening tree in industrial and traffic-polluted areas. S. alba and S. sorbifolia are sensitive to dust pollution, so they can be used as sensitive tree species to indicate atmospheric dust pollution. Our results may help design a feasible approach for urban shrub greening.