Vehicle Flow Research Articles

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

The influence of the existence of new landmark on urban spatial

The Old City Museum, located in the middle of the Bubakan Roundabout, is one of the new landmarks in Semarang City due to its magnificent existence and attractive architectural design. However, the existence of this museum has changed the urban spatial in the area, such as flow of vehicles around the museum, making it difficult for people to access detours because the area is no longer in the form of a roundabout, and also the unavailability of special parking spaces for this museum makes visitors forced to park on the shoulder of the road or in the shophouses around the roundabout. Therefore, in accordance with this phenomenon, this study aims to determine the significant existence of this museum to the spatial layout in the Bubakan area, Semarang, seen from the eight of urban design elements. The qualitative method was used to find answers to phenomena and problems in the field. Observation and interview with tourists and local people were conducted to get the information related the effectiveness of urban spatial in Bubakan Area, Semarang. The result showed the importance of the existence of this museum and provided suggestions to make it more conducive.

Modeling the lane-changing behavior of non-motorized vehicles on road segments via social force model

Using traffic marking to separate non-motorized lane from the adjacent motorized lane is a common traffic design method for urban road segments. With the increase in non-motorized vehicle flow, illegal lane-changing occurs frequently, which leads to more serious conflict among road users. The study of the behavior characteristics of non-motorized illegal lane-changing on road segments, and establishing a microscopic simulation model can be used as a theoretical support and analysis tool for the design of non-motorized lane. This paper takes non-motorized vehicles as the research object to study lane-changing behavior characteristics. Then, the K-means clustering algorithm is used to classify riding styles into three types: risker, opportunist, and prudent. The micro-behavioral characteristics of riders with different riding styles are analyzed. Next, a three-layer model framework composed of perceptual, decision-making, and social force model is established. The logit model is used in the decision-making model to predict the probability of non-motorized vehicles crossing the line. Different riding styles are considered in the social force, and a virtual boundary force is introduced to describe lane-changing behavior. Subsequently, parameters are calibrated and the model is validated in various ways. Finally, the proposed model is used to conduct simulation experiments to explore the renovation scheme of the non-motorized lane. The results show that the proposed model can reproduce the traffic operation in the real scenario.

Traffic Prediction using Machine Learning

The traffic is one of the major issues faced by people in almost every city. Predicting the traffic has always been very challenging for transportation planning as well as the city manager. The growth of population and usage of vehicles has increased the need for a reliable traffic prediction system. The system should reduce congestion, avoid accidents, and optimize the traffic flow that enhance road safety. The use of machine learning algorithms would be more suitable in handling the traffic and maintaining the flow of vehicles. The paper focuses on the work related to handling the traffic using machine learning principles such as LSTM and Random Forest (RF) algorithm. The advanced online dataset for traffic forecasts is used. The system is tested to have improved accuracy with the help of feature engineering. The basic dataset had the following fields: Vehicle Id, Time, Date, Junction Id are the primary and significant parameters considered to anticipate the traffic at each zone. With this we have added the day and weekend component to enhance our result.

Sustainable collaborative strategy in pharmaceutical refrigerated logistics routing problem

The rapid growth of pharmaceutical refrigerated logistics poses sustainability challenges, including elevated costs, energy consumption, and resource inefficiency. Collaborating multiple depots can enhance logistics efficiency when standalone distribution centres have limited transport resources, i.e. refrigerated vehicles. However, the sustainable benefits and performance across different strategies remain unexplored. This study fills this research gap by addressing a refrigerated pharmaceutical routing problem. While many collaborative strategies prioritise economic and environmental benefits, our approach highlights a vital social indicator: maintaining vehicle flow equilibrium at each depot during collaboration. This ensures the stability of transport resources for all stakeholders, promoting sustainable collaborative logistics. The problem is formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Three collaborative strategies using Clustering VRP (CLUVRP) and improved Open VRP (OVRP) are proposed and compared. We develop two approaches to address traditional OVRP limitations in ensuring vehicle flow equilibrium at each depot. Our models consider perishable pharmaceuticals and time-dependent travel speeds. Three hybrid heuristics based on Simulated Annealing and Variable Neighborhood Search (SAVNS) are proposed and evaluated for efficacy. Computational experiments and a case study demonstrate distinct sustainable benefits across various strategies, offering valuable insights for decision-makers in the refrigerated logistics market.

Do women perceive pedestrian path attractiveness differently from men?

The objective of this work is investigating on differences in pedestrians’ perceptions in terms of gender. Specifically, we want to verify differences between male and female in perceiving aspects characterizing pedestrian paths and influencing their satisfaction, perceived service quality, and behavioural intentions. We propose a methodology based on Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. From the model calibration we derived different coefficients for male and female, representing the importance that each category of users gives to the various aspects characterizing the paths. Data were collected through a sample survey addressed to pedestrians walking on paths placed in an urban area of the southern Italy. We discovered interesting findings about gender differences. In general, females are more sensitive than males concerning many important aspects such as environmental aspects in terms of air pollution and noise, or personal security from robbery or theft, or presence of furnishing along the path and cleanliness of the environment. On the other hand, male consider more the continuity of pedestrian path and the condition of the walking surface, as well as the nuisance due to the vehicle flow. In terms of similarities in perceptions, male and female equally appreciated the comfort due to the presence of trees providing shade along the path. The findings from the model could be useful for planners, who should invest on those aspects considered as important by both the categories, but also on the aspects that determine an inequality between male and female. As an example, since women do not feel secure along those paths, planners could improve this aspect in such a way as to make paths attractive to both women and men, as well as the aspects considered more relevant for men, to incentivize pedestrian mobility.

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model.

An exposure-based traffic assignment (TA) model is used to quantify primary and secondary fine particulate matter (PM2.5) exposure from on-road vehicle flow on the Chicago Metropolitan Area regional network. PM2.5 exposure due to emissions from light-duty vehicles, heavy-duty trucks, public transportation, and electricity generation for electric vehicle charging and light-rail transportation is considered. The model uses travel demand data disaggregated by time-of-day period and vehicle user class to compare the exposure impacts of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a system optimal with respect to pollutant intake (SOI). Estimated baseline PM2.5 exposure damages are $3.7B-$8.3B/year. The SOI uses exposure-based vehicle rerouting to reduce total damages by 8.2%, with high-impacted populations benefiting from 10% to 20% reductions. However, the SOI's rerouting principle leads to a 66% increase in travel time. The model is then used to quantify the mitigation potential of different exposure reduction strategies, including a bi-objective optimization formulation that minimizes travel time and PM2.5 exposure concurrently, adoption of a cleaner vehicle fleet, higher public transportation use, particle filtration, and exposure-based truck routing. Exposure reductions range between 1% and 40%, but collective adoption of all strategies would lead to reductions upward of 50%.

Optimal dispatch model for aviation-fuel vehicles in refuelling operations

Taiwan Taoyuan international airport has been devoted to becoming a green airport in recent years, aiming to reduce carbon dioxide emissions (CDEs) from ground handling services. Refuelling operations play a pivotal role in airport operations and, given the short and frequent nature of flight takeoffs and landings, demand substantial work. Different aircraft types and flight lengths require varying refuelling operations, and the inefficient and unrealistic dispatch of aviation-fuel vehicles can lead to flight delays. In this study, time–space network flow techniques were used to formalise the flow of aviation-fuel vehicles in both space and time. Based on the problem characteristics and by adding appropriate constraints, the aim of this work was to minimise CDEs and construct an optimal model for the refuelling operation scheduling of aviation-fuel vehicles. The proposed model was evaluated through a case study, and conclusions and recommendations are presented.

A flow‐based model for the multivehicle covering tour problem with route balancing

AbstractThis paper introduces a new flow‐based mathematical formulation for the multivehicle covering tour problem. This problem determines a set of vehicle routes for the available fleet; therefore, the total distance traveled is minimal. In the proposed mathematical formulation, each vertex represents a location of interest, and these vertices belong to three sets: vertices that can be visited; vertices that must be visited; and vertices that must be covered, in the sense that each vertex must be close to a visited vertex in a route. The proposed formulation aims to find solutions with balanced routes (all the routes with an equal or similar number of visited nodes) and extends the previous formulations proposed in the literature by considering a two‐index vehicle flow formulation with a decision variable to find the balanced routes. It constitutes an NP‐hard integer programming problem based on a two‐commodity flow model, which is solved by applying the branch‐and‐cut and biased random‐key genetic algorithms. This paper reports computational results for some adapted instances from TSPLIB to evaluate the efficiency of the proposed model. The computational results validate the branch‐and‐cut and genetic algorithms, whereby the latter performs better in large‐size instances with reduced computational runtime. Finally, the used approaches obtained competitive solutions compared to the literature.

Evaluation of Motorway Lane Control Strategies for Mixed Flow of Autonomous and Human-Driven Vehicles

Evaluation of Motorway Lane Control Strategies for Mixed Flow of Autonomous and Human-Driven Vehicles

Analysis of pedestrian illegal crossing at unmarked segments: Environmental factors, pedestrian characteristics and crossing behaviours

Pedestrian violations are a major cause of pedestrian accidents. Considering that pedestrians are vulnerable to mortality and severe injury in traffic accidents, it is necessary to conduct a study of the factors influencing the occurrence of unmarked street crossing violations and to improve pedestrian safety in a targeted manner. This paper investigated pedestrian spatial violations on unmarked roadways at six high-traffic locations in Hefei and examined the effects of the road environment, pedestrian crossing behaviour, and pedestrian characteristics on pedestrian spatial violation decisions. Thirty variables with a total of 723 samples were obtained by an unmanned aerial vehicle (UAV) and a camera. The purpose of this study was to gain an understanding of pedestrian spatial crossing violations at unmarked roadways. A random parameter logit model was used to establish a pedestrian crossing decision model for roadway violations. The results indicate that males are more likely to violate than females; the probability of violation with companions is higher than that of crossing alone; when pedestrians cross with other strangers in the same direction, the rate will decrease; and shorter intersection spacing, a higher vehicle flow, and a longer pedestrian crossing length all reduce the illegal crossing propensity. The findings should be indicative to help traffic road construction departments and traffic participants take effective measures to improve road safety.

Combining SysML and Timed Coloured Petri Nets for Designing Smart City Applications

A smart city is an urban centre that integrates a variety of solutions to improve infrastructure performance and achieve sustainable urban development. Urban roads are a crucial infrastructure highly demanded by citizens and organisations interested in their deployment, performance, and safety. Urban traffic signal control is an important and challenging real-world problem that aims to monitor and improve traffic congestion. The deployment of traffic signals for vehicles or pedestrians at an intersection is a complex activity that changes constantly, so it is necessary to establish rules to control the flow of vehicles and pedestrians. Thus, this article describes the joint use of the SmartCitySysML, a profile proposed by the authors, with TCPN (Timed Coloured Petri Nets) to refine and formally model SysML diagrams specifying the internal behaviour, and then verify the developed model to prove behavioural properties of an urban traffic signal control system.

Analyzing the Influence of Vehicular Traffic on the Concentration of Pollutants in the City of São Paulo: An Approach Based on Pandemic SARS-CoV-2 Data and Deep Learning

This study employs surface and remote sensing data jointly with deep learning techniques to examine the influence of vehicular traffic in the seasonal patterns of CO, NO2, PM2.5, and PM10 concentrations in the São Paulo municipality, as the period of physical distancing (March 2020 to December 2021), due to SARS-CoV-2 pandemic and the resumption of activities, made it possible to observe significant variations in the flow of vehicles in the city of São Paulo. Firstly, an analysis of the planetary boundary layer height and ventilation coefficient was performed to identify the seasons’ patterns of pollution dispersion. Then, the variations (from 2018 to 2021) of the seasonal average values of air temperature, relative humidity, precipitation, and thermal inversion occurrence/position were compared to identify possible variations in the patterns of such variables that would justify (or deny) the occurrence of more favorable conditions for pollutants dispersion. However, no significant variations were found. Finally, the seasonal average concentrations of the previously mentioned pollutants were compared from 2018 to 2021, and the daily concentrations observed during the pandemic period were compared with a model based on an artificial neural network. Regarding the concentration of pollutants, the primarily sourced from vehicular traffic (CO and NO2) exhibited substantial variations, demonstrating an inverse relationship with the rate of social distancing. In addition, the measured concentrations deviated from the predictive model during periods of significant social isolation. Conversely, pollutants that were not primarily linked to vehicular sources (PM2.5 and PM10) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model.

A New Traffic System on Driver Sensitivity and Safe Distance Headway

A new macroscopic traffic system is devised that observes the transition distance between the vehicles and driver sensitivity during traffic evolution. The driver sensitivity in this system is based on the traversed time over a 200 m road section and speed (velocity). In addition, the proposed system considers the safe distance headway as the distance between vehicles changes. An analogy system for vehicle flow behavior is devised from a spring–mass system with changes in traffic. The proposed system can characterize traffic evolution for small and large changes in density. Furthermore, the changes in the travel of traffic rearwards during congestion and forward during smooth flow are dependent on driver sensitivity, transition distance, and safe distance headway. The proposed traffic system is hyperbolic. The Payne Whitham traffic system is based on uniform constant velocity for different conditions, which characterizes traffic evolution unrealistically. The proposed traffic system and the Payne Whitham system are assessed over a 2000 m circular road for large changes in density in two examples. Both the Payne Whitham and proposed traffic systems are numerically implemented with the first order centered scheme in Matlab. The discretization stability of both systems is enforced with the Courant–Friedrich–Levy (CFL) condition. The proposed system with lower driver sensitivity evolves with larger changes, whereas the proposed system with larger density has smaller changes in density and velocity. The simulation results showed that the traffic evolution with the proposed system is more appropriate than with the Payne Whitham system.

Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

This paper proposes models and algorithms for the estimation of traffic flows generated by the centralised management of autonomous vehicles under exclusive and mixed traffic conditions. It is assumed that the autonomous vehicle manager wants to obtain the system optimum that minimises the total travel time on the network, of autonomous vehicles in the case of exclusive traffic, and of all flows in the case of mixed traffic. In the case of mixed traffic, the drivers of the human-guided vehicles choose their routes from a user-optimal perspective; in this case, a fixed-point problem has to be solved, which represents a state of equilibrium between the flows of human-guided vehicles, the flows of autonomous vehicles, which follow paths aimed at the system optimum, and the link costs. The proposed models and algorithms are tested on a small network with a single origin-destination pair, and on the Sioux Falls network, with different levels of congestion and different percentages of autonomous vehicles. The results show the validity of the proposed methods and the impact of autonomous vehicles on network performance. The proposed static/equilibrium approach is necessary to evaluate transport planning, design or policy interventions that include the presence of autonomous vehicles in traffic flows.

ІНЖЕНЕРНИЙ МЕНЕДЖМЕНТ МОНІТОРИНГУ ПОТОКІВ ТРАНСПОРТНИХ ЗАСОБІВ ПРИ ЗБИРАННІ ЗБІЖЖЯ

The results of using equipment for determining the location of vehicles based on global navigation sat-ellite systems to modernize field road networks and improve the quality of vehicle flow monitoring systems during grain harvesting are presented. An engineering solution for optimizing vehicle registration points is presented and substanti-ated, which allows to eliminate congestion at entrances and/or exits by reducing the cost of the infrastructure required for operation. A list of measures for implementing an improved toll collection system has been formulated, including: installation of access points at entrances and exits, creation of special data centers, development of machine code and reflashing of previously released devices. To verify the proposed solution, data transmitted from vehicles to a GSM base station based on the mass service system. Keywords: grain harvesting, monitoring, flow, vehicle, queuing system.

A tensorial weighted Schatten-[formula omitted] norm model with neighbor regularization for traffic data completion and traffic system correlation exploration

Traffic data exhibit the spatiotemporal heterogeneity. Revealing traffic system correlations (TSC) from traffic data, which refers to discovering inter-station correlations incurred from movements of passengers, is a meaningful task for releasing traffic congestion and sustainable urban operations. Since traffic data involve a great loss or corruption due to equipment and transmission failures, traffic data completion (TDC) serves as a necessary task for TSC exploration. We observe that: for the TDC task, traffic data exhibit anisotropic periodicity and repetition along different temporal dimensions, which cannot be captured by traditional low-rank TDC methods; for the TSC exploration task, a traffic network obeys graphical structure and exhibits a strong spatial correlation between stations or road segments. Based on these observations, we propose a Tensorial Weighted Schatten-pNorm model with Neighbor Regularization (TWSN2R) for accomplishing the two tasks simultaneously. TWSN2R consists of a tensorial weighted Schatten-p norm term and a neighbor regularization term. The first term mines the anisotropic periodicity with a tensor form of weighted Schatten-p norm which preserves suitable rank components of traffic data by allocating different weights in both tensorial dimension and decomposed mode dimension. The second term mines a spatial correlation of a traffic network by enforcing a neighbor contribution of passenger flows incurred from the network, and extrinsically reveals an inter-station (or inter-road-segment) TSC of the network. Experiments on the subway flow dataset MetroBJ and the road network vehicle speed dataset RoadBJ demonstrate that, TWSN2R not only outperforms several state-of-the-art TDC methods, but also reveals two pieces of regulations of TSC. (a) For subway passenger flows, positive correlated stations and negative correlated stations contribute to passenger flows differently according to each station’s attributes. (b) For road networks, urban loops, expressways and transportation hubs contribute to the vehicle flows at a majority, and serve as the critical roads for mitigating the congestion of whole road networks.

A reduced-order model for fast predicting ionized flows of hypersonic vehicles along flight trajectory

An improved Reduced-Order Model (ROM) is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows. This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition (POD) on snapshots and is coupled with the Radial Basis Function (RBF) to achieve fast prediction speed. However, due to the disparate scales in the ionized flow field, the conventional ROM usually generates spurious negative errors. Here, this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM. Then, extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy. In addition, to construct high-efficiency snapshots, a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed. To evaluate the performance of the proposed fast prediction method, two hypersonic vehicles with classic configurations, i.e. a wave-rider and a reentry capsule, are used to validate the proposed method. Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth. Compared with the conventional ROM prediction, the prediction results are significantly improved by the proposed method around the discontinuities, e.g. the shock wave and the ionized layer. As a result, the proposed fast prediction method reduces the error of the conventional ROM by at least 45%, with a speedup of approximately 2.0 × 105 compared to the Computational Fluid Dynamic (CFD) simulations. These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.

Analysis of left-turn behaviors of non-motorized vehicles and vehicle-bicycle conflicts.

In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.

Evaluation Of The Application Of The Zoning System On Vehicle Flow Patterns At The Torobulu Ferry Port

Torobulu Ferry Port is a port located in Laeya District, South Konawe Regency, Southeast Sulawesi Province. This port connects between Konawe Selatan and Muna districts. At this time, the Torobulu Ferry Port has not implemented a zone system in accordance with applicable regulations, which causes many local residents to carry out activities in the port area, delivery vehicles or pick-ups are free to enter the port dock. a parking area ready to load vehicles and a parking area for shuttle vehicles, making delivery/pick-up vehicles often park in a ready-to-load parking area. To analyze this, an analysis based on the Minister of Transportation Regulation Number 29 of 2016 concerning Ferry Port Sterilization is used to analyze the zone system and the Decree of the Director General of Land Transportation Number SK.242/HK.104/DRJD/2010 concerning Technical Guidelines for Traffic Management Crossing for analysis of vehicle traffic flow patterns as well as analysis of the need for traffic signs supporting the zoning system and flow patterns referring to the Regulation of the Minister of Transportation Number 13 of 2014 concerning Traffic Signs. In order to create an orderly and safe ferry port, the results of the analysis are to establish a zoning system and regulate the traffic flow patterns of vehicles at the Torobulu Ferry Port and to support the zoning system and vehicle flow pattern settings, it is necessary to optimize facilities such as: adding tollgates, determination of parking areas for passenger/pick-up, determination of parking areas for ready-to-load vehicles, and traffic signs.