Field Traffic Data Research Articles

A Study on Environmental Impact of Slow Moving Electric Vehicles Using Microsimulation on Lucknow Urban Road With an On-Ramp.

The adoption of electric vehicles for mobility is seen as a major step towards the conservation of the environment. In India, slow-moving Electric 3-Wheelers (E3Ws) have been adopted for last-mile connectivity. The present study investigated the impact of slow-moving electric 3-wheelers on the environment in terms of emissions and traffic performance in mixed conditions. Field traffic data from a section of road in the city of Lucknow was collected and used for the calibration of the traffic model. A total of 6 scenarios were tested using traffic modelling in the open-source microsimulation software SUMO. Krauss model was used to model mixed traffic and HBEFA 4 was used to calculate the emissions of fuel-driven vehicles. In each scenario, the volume of fuel-driven vehicles was kept constant and the volume of E3Ws was varied. For the last 2 scenarios, E3Ws were replaced with modified Electric 3-wheelers (ME3Ws) and Electric Buses. Initial findings showed that the average emission decreased as the number of slowly moving electric vehicles increased, but the average flow and harmonic mean speed decreased by 49.8% and 28.8%, respectively, despite keeping the original composition of fuel-driven vehicles the same in every scenario. Further analysis of scenarios revealed a strong correlation () between the reduction in the number of vehicles and the reduction in emissions like Carbon Dioxide (), which is responsible for global warming. Scenarios in which faster electric vehicles and electric buses replace slow-moving E3Ws also demonstrate emission reduction without noticeably affecting traffic performance parameters. The study shows that the environmental benefits of E3Ws in a limited section of Lucknow road are offset by their low-speed capability. Hypothetical scenarios wherein Modified E3Ws and Electric Buses were introduced reported benefits both in terms of emissions and traffic performance.

A Case Study on Reducing Traffic Congestion–Proposals to Improve Current Conditions

Traffic congestion has been considered one of the most serious global issues confronting all nations in recent years. Basrah City’s highways in general, and particularly the highways surrounding and leading to the University of Basrah, the campus of Bab Azzubair, and the college complex, have experienced traffic congestion and higher delays. The focus of this study is to propose some solutions to mitigate traffic congestion and higher delays on these highways. Field traffic data and questionnaires where SPSS was used to analyse the collected data, and Highway Capacity Software (HCS 2010) was used to assess the level of service (LOS) on the highways. The results showed that the LOS reaches level F on Baghdad Street, while other streets range from level B to level C. Two proposals have been suggested to improve the LOS on Baghdad Street: (1) applying a park-and-ride system; and (2) widening Baghdad Street by adding two lanes in each direction. The LOS for both proposals has been evaluated, and the results showed that the first proposal performed better. The analysis of the collected data also showed that public transportation services are very limited on the highways surrounding and leading to the Bab Azzubair campus. Therefore, public transportation services should be promoted and enhanced in Basrah City. Doi: 10.28991/CEJ-2023-09-10-07 Full Text: PDF

A Traffic Flow Dependency and Dynamics based Deep Learning Aided Approach for Network-Wide Traffic Speed Propagation Prediction

The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a traffic flow dependency and dynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v-CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v-CTM. In this way, we well couple the physical models (traffic dependency and v-CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v-CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy.

Weather-Based Lane-Change Microsimulation Parameters for Safety and Operational Performance Evaluation of Weaving and Basic Freeway Segments

It is well recognized that adverse weather conditions have significant negative impacts on safety and mobility of transportation systems. Microsimulation modeling has emerged as a cost-effective tool to quantify the safety and operational effects arising from adverse weather. Developing a realistic microsimulation model necessitates adjusting driving behavior models through trajectory-level data. This study contributes a methodology to update lane-change parameters to develop weather-specific microsimulation models based on different freeway facilities, and ultimately evaluate the safety and operational performance of the roadways. Representative parameters in various weather and facility types were extracted using an automated process. As part of the comprehensive assessment of the adjusted parameters, a weaving section and a basic freeway segment on Interstate 80 in Wyoming were identified as potential candidates. The safety and operational analyses were conducted using VISSIM. Various simulation scenarios were designed based on the field traffic flow data. The safety analysis using three surrogate measures of safety including time-to-collision, deceleration rate to avoid collision, and post encroachment time revealed that adverse weather generated a higher number of conflicts than did clear weather for both facilities. The operational analysis suggested that adverse weather produced lower average speed and higher total travel time and delay than clear weather. The demonstrated methodology could be used in assessing various connected vehicle applications associated with lane change in microsimulation from safety and operational perspectives and could be adopted by transportation agencies to develop weather-based microsimulation models.

Analysis of Traffic Operation Characteristics and Calculation Model of the Length of the Connecting Section between Ramp and Intersection

Many cities have built expressways to alleviate traffic congestion, among which elevated expressways are the most common form. However, traffic congestion still occurs frequently in the connecting section between the ramp of expressway and the ground intersection. Based on the field traffic survey data, the traffic operation characteristics of vehicles in the connecting section and the main factors affecting the length of the connecting section are analyzed. A combined model for calculating the length of the connecting section between ramp of urban expressway and intersection is proposed. VISSIM is used to simulate the traffic flow under the current and calculated length of the connecting section. The comparison results show that under the calculation length, the travel time, average delay, parking time and queue length are reduced to varying degrees, which verifies the rationality of the calculation model.

Multi-class traffic flow model based on three dimensional flow–concentration surface

This paper proposes a continuum model based on a three-dimensional flow-concentration surface for multi-class traffic. The model assumes that the flow of any vehicle class is a function of the class density as well as the fraction of road area occupied by other vehicle classes. By considering occupancy of road area instead of lane occupancy, the model effectively describes traffic flow that does not follow lane discipline. The propagation speed of small disturbance (PSSD), conventionally defined from the two-dimensional flow–density relationship, is reformulated for each class using a three-dimensional flow–concentration surface. Using the proposed PSSD and a speed–area occupancy (speed-AO) relationship, a second-order continuum model for multi-class traffic is formulated. The speed–AO relationship captures class-specific congestion and replicates the gap-filling behaviour commonly observed in lane-indisciplined traffic. Properties of the proposed model are validated theoretically where possible, and through numerical simulation when theoretical derivations are cumbersome. Numerical simulation of the proposed multi-class traffic model replicates field-observed phenomena such as shockwaves and rarefaction waves, local cluster effect, and gap-filling behaviour. Finally, the model is calibrated using field traffic data collected on a road section with bottleneck, and is found to replicate class-wise vehicle flows and speeds, and stop-and-go phenomena.

Estimating passing sight distances for overtaking truck platoons – Calibration and validation using VISSIM

Two-lane highways comprise a substantial proportion of the nation’s highways. One of their critical aspects is the passing sight distance (PSD), which is the minimum distance required for executing a safe passing maneuver using the opposing travel lane. Sight distances that are shorter than the available PSD warrant the designation of no-passing zones. The PSDs, documented in the Manual on Uniform Traffic Control Devices, are developed based on the premise that only one slower moving impeding vehicle is being overtaken. Yet, the oil and gas industry in Wyoming advises its truck drivers to travel in platoons creating a complicated situation for trailing vehicles. Passing a single impeding vehicle is cumbersome, not to mention passing multiple trucks. The PSD is augmented by the length of each truck in the platoon and the spacing among the trucks. In this research, an existing mathematical kinematics PSD model is implemented to compute the PSDs needed to pass multiple oil and gas trucks for a variety of scenarios belonging to two-lane highway segments with posted speed limits of 70 mph. The PSDs computed range from 1385 to 1915 ft depending on the number of trucks in the platoon and spacings between the trucks. The results are compared with those of VISSIM microsimulation models, which are validated using field traffic volume data. The discrepancies between the computed PSD results and those of the simulations are less than 3%. Furthermore, it is recommended to deploy passing lanes to mitigate the effects of truck platoons on traffic operations.

Non-Linear Evaluation Model to Analyze Saturation Flow under Weak-Lane-Disciplined Mixed Traffic Stream

Saturation flow is an essential parameter for the performance evaluation and signal cycle design of an intersection. However, in a weak-lane-disciplined mixed traffic stream, vehicles (mainly motorized two- and three-wheelers) are more interested in forming queues with the behavior of creeping and infiltration through the larger vehicles for a quick dispersion. Consequently, a high rate of discharge can be found in the first few seconds of green, followed by a wavering discharge pattern to the end of the green period. In this context, the present study frames a non-linear evolutionary model (NLEM) to analyze passenger car unit (PCU) and saturation flow value by minimizing the vehicle encroachment and wavering in discharge for mixed traffic stream. Field traffic data was collected from 10 signalized intersections from three different cities of India. The NLEM considers the vehicle discharge during the saturated green interval in the optimization process to analyze PCU and saturation flow value. The result indicates an appropriate estimation of PCU and saturation flow concerning the Highway Capacity Manual. The discharge profile obtained using the proposed model also specifies the model’s aptness in analyzing the effect of vehicle encroachment and wavering throughout the green period. Thus, the proposed NLEM is probably suitable for analyzing the PCU and saturation flow of an intersection where vehicle interaction is sensitive because of a mixed scenario.

A dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles

This paper describes a dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles (CAVs). The proposed dynamic lane-changing trajectory planning (DLTP) model adopts vehicle-to-vehicle (V2V) communication to generate an automated lane-changing maneuver with avoiding potential collisions and rollovers during the lane-changing process. The novelty of this method is that the DLTP model combines a detailed velocity planning strategy and considers more complete driving environment information. Besides, a lane-changing safety monitoring algorithm and a lane-changing starting-point determination algorithm are presented to guarantee the lane-changing safety, efficiency and stability of automated vehicles. Moreover, a trajectory-tracking controller based on model predictive control (MPC) is introduced to make the automated vehicle travel along the reference trajectory. The field traffic data from NGSIM are selected as the target dataset to simulate a real-world lane-changing driving environment. The simulations are performed in CarSim-Simulink platform and the experimental results show that the proposed method is effective for lane-changing maneuver.

Congestion recognition for hybrid urban road systems via digraph convolutional network

Congestion recognition is the prerequisite for traffic control and management, vehicle routing, and many other applications in intelligent transportation systems. Different types of roads with traffic facilities provide multi-source heterogeneous field traffic data, which contain the fundamental information and distinct features for congestion recognition. To exploit these traffic big data, in this paper, we propose a machine learning-based framework to tackle the congestion recognition problem. It can be divided in two parts, a digraph-based representation for hybrid urban traffic network and a Dirgraph Convolutional Neural Network (DGCN)-based learning model. At first, the representation incorporates the fundamental traffic variables with the correlation of different traffic flows, and partially decouples the global network topology from local traffic information. And then, to proceed with digraph-based samples, a new type of graph feature extraction method is introduced and the graph Fourier transform is defined accordingly. This distinguishes the proposed model from the conventional graph convolutional networks. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing congestion recognition methods.

Traffic State Recognition of Intersection Based on Image Model and PCA Hashing

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

Hybrid Dynamic Traffic Model for Freeway Flow Analysis Using a Switched Reduced-Order Unknown-Input State Observer.

This paper introduces a new methodology for reconstructing vehicle densities of freeway segments by utilizing the limited data collected by traffic-counting sensors and developing a macroscopic traffic stream model formulated as a switched reduced-order state observer design problem with unknown or partially known inputs. Specifically, the traffic network is modeled as a hybrid dynamic system in a state space that incorporates unknown inputs. For freeway segments with traffic-counting sensors installed, vehicle densities are directly computed using field traffic count data. A reduced-order state observer is designed to analyze traffic state transitions for freeway segments without field traffic count data to indirectly estimate the vehicle densities for each freeway segment. A simulation-based experiment is performed applying the methodology and using data of a segment of Beijing Jingtong freeway in Beijing, China. The model execution results are compared with the field data associated with the same freeway segment, and highly consistent results are achieved. The proposed methodology is expected to be adopted by traffic engineers to evaluate freeway operations and develop effective management strategies.

A MODELING FRAMEWORK OF HIERARCHICAL EARTHQUAKE RELIEF CENTER LOCATIONS UNDER DEMAND UNCERTAINTY

This paper presents a modeling framework for locating earthquake disaster relief centers, developed through multidimensional and integrated perspectives of transportation engineering and humanitarian logistics planning. The proposed framework consists of three sequential steps: 1) Seismic risk analysis, to evaluate the vulnerability of both road network and area-covering disruptions, using the spaghetti and meatballs method, which is a geographic information system (GIS) based analytical approach enriched with historical earthquake statistics and earthquake fault data; 2) Travel demand analysis, to forecast travel demand, travel behavior, travel pattern, and traffic volume, using the four-step transportation model developed based on field traffic survey data and seismic risk analysis data; and 3) Facility location problem analysis, to locate optimal earthquake disaster relief centers, using the hierarchical location problem model formulated based on a meta-heuristic genetic algorithm (GA) optimization technique. To evaluate model mechanism and performance, a preliminary model was then applied to the simulated geographical area with simulated road network of Chiang Rai Province, Thailand. As a result, two-level hierarchical disaster relief centers in response to earthquakes, taking into account accessibility and functional ability of transportation networks, risk covered/uncovered demand and supply distribution, can be viably determined. In the upperlevel, a central disaster relief center is functioned to collect rescue equipment and survival bags from both inside and outside the area and to distribute those to local disaster relief centers in the area. In the lowerlevel, three local disaster relief centers were optimally located, functioned to receive supplies from the central disaster relief center and then distribute to demand points affected by earthquakes. The resulting model can provide government and related agencies profound information in planning and developing either pre-disaster or post-disaster operations.

Hierarchical ramp metering in freeways: An aggregated modeling and control approach

This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections.Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method.The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g.CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways

Congestion has become a significant issue in recent years and has greatly affected the efficiency of urban traffic operation. Random and disorderly lane-changing behavior greatly reduces traffic capacity and safety. This paper is mainly concerned with the relationship of lane-changing spacing intervals provided by off-ramp facilities and traffic flow conditions. Through field investigations in Beijing, several typical lane-changing behaviors at off-ramp areas are analyzed. By using field traffic data and actual road geometry parameters, VISSIM-based micro-behavior simulations at off-ramp areas are implemented to obtain traffic flow conditions with different lane-changing spacing intervals and other model parameters, such as traffic volume and ratio of off-ramp vehicles. Then, the numerical relationships between traffic flow state and model parameters can be shown. The results show that with increasing traffic volume and the ratio of off-ramp vehicles, the lane-changing spacing interval required by vehicles should be increased. For the same ratio of off-ramp vehicles, if the traffic volume increases by 100 pcu/h/lane (pcu is a unit to stand for a standard passenger car), the corresponding lane-changing spacing interval should be increased by a spacing of 50–100 m to avoid increasing congestion. Based on the results of this paper, smart lane management can be implemented by optimizing lane-changing spacing intervals and lane-changing behaviors to improve traffic capacity.

A System Dynamics Simulation to Estimate Roundabout Entry Capacity by Adopting Three Different Methods

Many models were introduced to estimate the roundabout entry capacity from 1980s until now. In the United States, transportation agencies adopted three different models from 1994 until 2010. The Austroads, the UK, and the HCM 2010 methods. In this paper, these three methods were used to analyze the field traffic data of Al-Quds roundabout, located near Baqubah City, simultaneously by utilizing a system dynamics model. The collected data included turning movements, circulating flows, and field calculated entry capacities whenever possible during the observation period which lasted for 14 consecutive hours. A comparison is then conducted on the resulting entry capacities and their variation over time. The results showed that the calculated capacity according to Austroads method is the highest at all times while the UK method was always a little lower and the HCM 2010 method was always the least on all entries. The UK method capacity estimates were the closest to the field measured capacities for they returned the least RMSE on all entries. Field capacities showed some tendency towards the Austroads results in the north and south bound entries which carry about 66% of the total traffic. While, field capacities showed more proximity towards the HCM capacity results in the east and the west bound entries which carry 34% of the total traffic.

Evacuation time at Jong-no 3ga subway station considering electric train delays and congestion

Subway delays and congestion can cause evacuation times exceeding the safety limits given the accumulation of passengers on platforms and within trains. In this study, we conducted evacuation simulations to determine the increase in evacuation time and analyze associated risks caused by delay and congestion. Evacuation calculations used the Fermi estimate based on field survey and traffic data. The Pathfinder software was employed for evacuation simulation analysis, where the maximum acceptable evacuation time in the worst case was set to 6 min following engineering considerations of station characteristics and according to the domestic railway guide. We found that a single delay increased the evacuation time by 119%, whereas two delays reached a 133% increase. The evacuation time increased by 165% when considering the maximum congestion of electric trains at 192%. Therefore, measures to reduce subway delays and limit the number of people in electric trains at a given time should be adopted. Moreover, risks during subway station evacuation should be further analyzed to create plans considering structural and environmental characteristics.

Analysis of drivers’ performance in response to potential collision with pedestrians at urban crosswalks

Protection of vulnerable road users has been an increasingly important requirement for today's autonomous vehicles. Analysis of drivers’ response to potential collision with pedestrians can provide guidance for vehicle evasive manoeuvre and data support for traffic collision modelling. Field traffic data were collected by video recording and image processing at urban midblock crosswalks in Beijing, China and Munich, Germany. The drivers’ response to vehicle–pedestrian conflict is discussed by deceleration and acceleration phases considering the lane-based kinematics of vehicles. Some basic characteristics describing the driver behaviour in an evasive process are statistically analysed, including the yielding decision, space history of deceleration, deceleration rate choice, and accelerating behaviour. Results show significant differences in drivers’ yielding decisions. In addition, the onset of braking and the trend of the space history of average deceleration rate during the drivers’ approaching process have been quantitatively proposed. The contributing factors to driver's yielding behaviour are analysed by the Binary Logit Model. The behavioural differences address the needs to make intercultural adaptation when introducing new autonomous vehicle technologies to developing countries. This study provides a basis for establishing evasive decision corrections for drivers or driverless vehicles.

Calibration of microscopic traffic simulation models using metaheuristic algorithms

Abstract This paper presents several metaheuristic algorithms to calibrate a microscopic traffic simulation model. The genetic algorithm (GA), Tabu Search (TS), and a combination of the GA and TS (i.e., warmed GA and warmed TS) are implemented and compared. A set of traffic data collected from the I-5 Freeway, Los Angles, California, is used. Objective functions are defined to minimize the difference between simulated and field traffic data which are built based on the flow and speed. Several car-following parameters in VISSIM, which can significantly affect the simulation outputs, are selected to calibrate. A better match to the field measurements is reached with the GA, TS, and warmed GA and TS when comparing with that only using the default parameters in VISSIM. Overall, TS performs very well and can be used to calibrate parameters. Combining metaheuristic algorithms clearly performs better and therefore is highly recommended for calibrating microscopic traffic simulation models.

Vulnerability in Transport Network during Critical Infrastructure Renewal: Lessons Learned from a Dynamic Traffic Microsimulation Model

Abstract: This paper presents a Dynamic Traffic Assignment (DTA)-based microsimulation model that assesses traffic impacts on transport network during sudden closure of a critical infrastructure (CI) in Halifax, Canada. DTA models can replicate actual traffic conditions considering long queues and spillback. The model is calibrated and validated using field traffic data and used to predict traffic flows in the network. The model suggests an increment of 6 minutes in average traffic delay and 24.5% reduction in the number of arrived vehicles during the given incident. The model also reports spillover effects in the network.