Traffic Microsimulation Models Research Articles

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

A Study on Improvement to Increase Efficiency of U–Turn Opening on Charan Sanit Wong Road at Bang O Fire Station U–Turn

The U–Turn is a risky point where there are frequent road accidents. There are many factors leading to the problem of the U–turn such as the physical characteristics of the U–turn, location of U–turn median opening, insufficient sight distance, risk spot around the U–turn without traffic management and safety and factors caused by the driver. This paper presents the results of a study on improvement to increase efficiency of U–Turn Opening on Charan Sanit Wong Road at Bang O Fire Station U–Turn. By studying the improvement pattern of the U–turn from the past to the present and the pattern of improvement measure. Traffic microsimulation model was developed using VISSIM software and evaluated the traffic efficiency. The results were analyzed to determine the Time–To–Collision (TTC) using the SSAM program to assess traffic safety from the number of conflict points. The results showed the pattern of improvement measure with improved physical characteristics by increasing the length of the storage lane and restricted U–Turn for motorcycle measure could increase traffic efficiency. The average travel time was reduced by 1% and 10% and reduced the average delay by 12% and 31% in the directions to Rama VII and Pinklao, respectively. The safety analysis results showed the pattern of improvement measure which has a Time–To–Collision (TTC) value of 0.88 seconds and has the least number of conflicts point compared to previous improvement pattern.

Speed limit management on two-lane rural roads shared by drivers and cyclists to improve safety and traffic operation

Two-lane rural roads require management that integrates all road users, especially cyclists, who have reached significant traffic levels. Management measures must focus on increasing safety and, wherever possible, also improving traffic operation. Speed limit management can be a solution, however it has to be based on scientific criteria. In this study, various speed limits were simulated on a narrow rural road using a traffic microsimulation model and considering a set of scenarios combining motor and cycle traffic levels. As a results of simulations, performance measures were obtained to characterise safety and traffic operation. The optimal speed limit for each traffic level was obtained by a Multi Criteria Decision Making method, using performance measures as criteria. The results point to the groups of cyclists to ride two-abreast, and to a dynamic management of the road speed limit, reducing the speed during periods of peak motorised and bicycle traffic. It is recommended to adapt the study to each rural road considering its traffic and geometric characteristics, calibrating the traffic microsimulation model and obtaining a specific dynamic speed limit management model.

TomTom Data Applications for the Assessment of Tactical Urbanism Interventions: The Case of Bologna

This work aims to evaluate how a temporary school square implemented in the city of Bologna under the principles of the tactical urbanism approach impacted on vehicular patterns through exploiting TomTom Floating Car Data (FCD) from before and after the intervention. Such data, passively collected by vehicles acting as moving sensors on the network, have been used for the analyses instead of data collected through usual methods. After statistical validation of available datasets through two-tailed paired Student’s t-tests, trend analyses have been performed on sample sizes and speed-related values to detect global variations in the first place, and more thoroughly among clusters of road segments based on graph-calculated distance from the intervention site. Results suggest that traffic flows have been relocated from segments directly affected by the intervention, where a decrease has been registered (−23.87%), towards adjacent streets or segments in a buffer area, which have recorded an increase (+3.51% and +3.50%, respectively), so the phenomenon of traffic evaporation did not take place as opposed to more widespread tactical urbanism interventions described in the literature. OD matrices per 15-min time fractions over the three selected peak time slots have been extracted in order to obtain reliable input data for a future development of traffic microsimulation models. The extraction method is based on least squares optimization problems solving systems of linear equations representing OD flows assigned to the observed link, after selecting a set of k¯ shortest paths through a Path Size Logit (PSL) model. Even though the availability of large amounts of data could not overcome typical underdetermination of the problem, due to the key issue of data dependence among traffic counts, the validation of retrieved matrices returned good results in terms of correlation between observed and estimated link flows. In the few cases where the quality of correlation fell, underlying causes have been investigated and the influence of outliers, amplified by the high fragmentation of the provided road graph, might represent the core problem.

Exploring the effects of measures of performance and calibration strategies on calibrating traffic microsimulation model: A quantitative analysis approach

AbstractIn the subject of traffic microsimulation model (TMM) calibration, measure of performance (MoP) plays an essential role. However, due to the diversity of MoP types, choosing a MoP or MoP combination (usually used for multi‐criteria calibration strategy) that can represent the characteristics of field traffic operation has become the key to the calibration problem. This paper proposed a quantitative analysis approach (suitable, in general, for any TMM) with three aspects. Through this approach, more detailed and representative MoPs can be studied. At the same time, the effect of different calibration strategies with various MoP combinations on TMM calibration can also be compared comprehensively. The methodology is tested on a specific case study (a signalized link with a cyclic interrupted flow) by VISSIM, where various MoPs and calibration strategies (single‐criteria, the a priori‐based multi‐criteria, and the a posteriori‐based multi‐criteria calibration strategy) are implemented for comprehensive inspection and comparison. The results show that the TMM performance is clearly dependent on the MoPs and calibration strategies. Moreover, the a posteriori‐based multi‐criteria calibration strategy is more stable than the other two strategies for better performance TMM. The findings of this study provide new insights into the effects of MoPs and calibration strategies on TMM calibration.

Illegal Maneuver Effect on Traffic Operations at Signalized Intersections: An Observational Simulation-Based Before-After Study Using Response Surface Methodology

This study investigates three illegal maneuvers at signalized intersections: pedestrians jaywalking at signalized crosswalks (JSC), vehicles stopping near the intersections (SNI), and vehicles occupying the through lane instead of the left-turn lane to make a left turn (OTL). Traffic microsimulation models of four intersections were developed using Aimsun, and data were collected by a drone over a 3-hour period. The car-following model (Gibbs model) implemented in Aimsun was calibrated for each of the intersections and validated at the 95% confidence level. The validated Aimsun models were used to perform 13 experiments designed to investigate the interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption. These 13 experiments were identified using the design of experiments D-optimality criterion. To investigate the main and interaction effects of decreasing two or more illegal maneuvers on travel time and fuel consumption, the response surface methodology (RSM) was used. Using RSM, the statistical model that was found to best fit the simulation results was a quadratic form. The results showed that the dependent variables “travel time ratio” and “fuel consumption ratio” are affected not only by a decrease in violations ratio but also by the volume of traffic as the exogenous variable. It was found that decreasing two of the violations, namely, JSC and SNI, improves travel time and fuel consumption but decreasing OTL has the opposite effect, resulting from the inadequate design of left-turning lane length/capacity and/or inadequate signal timing to accommodate left-turning volume.

Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions

ABSTRACT This study combines an integrated transport, land-use, and energy (iTLE) modeling system with traffic microsimulation model and emission simulator for a holistic analysis of COVID-19 pandemic related changes in traffic flows and emissions. An activity-based travel demand model within iTLE informs pandemic traffic operation scenarios for traffic microsimulation modeling. Link-based simulation outputs inform a finer-grained emission estimation process within a MOtor Vehicle Emission Simulator. Results suggest that the overall network performance improves during lockdown as average delays and queue time decrease by 42.04% and 5.9% respectively compared to pre-COVID condition. Emission results reveal that GHG emissions significantly decrease (64%) in lockdown while it starts increasing gradually in post-pandemic period. Link-based emission analysis indicates that major arterial streets achieve a significant reduction in air pollutant emission. The findings of this study will help transportation planners, engineers, and policymakers to devise effective policies for the improvement of transport operations and emissions.

Comparison of microscale traffic emission models for urban networks

Traffic-related air quality issues remain in urban areas worldwide. For this reason, there is an increasing need to estimate the contribution of road traffic to atmospheric emissions at local level with high temporal and spatial resolution. Modal models compute emission rates as a function of specific engine or vehicle operating conditions at the highest resolution (seconds). They can be applied for microscale studies being a cost-effective tool to emulate differences in emissions levels in road networks. Two modal emission models, the Australian PΔP (Power-delta-Power) and the simplified version of the European PHEM (Passenger Car and Heavy-duty Emission Model), PHEM-light model, have been used. Also, a comparison to the cycle-variable emission model VERSIT+micro (Netherlands organisation for applied scientific research state of the art traffic emission model) has been performed. For the comparison of both modal models, the main variables involved in traffic emission calculation were identified. 1 Hz speed-time profiles for individual vehicles were generated with the traffic microsimulation model VISSIM (Vehrkehr in Statden SIMulation) for different traffic conditions. To understand the response of modal models, detailed estimations of NO X emissions and fuel consumption were compared for different vehicle classes. Instantaneous emission profiles for individual driving patterns are highly sensitive to speed-acceleration profiles, vehicle mass, and road gradient, which are essential variables for the emission calculation. Although there are differences between European and Australian models, engine power and load were used to map vehicle classes for a more consistent comparison. It is essential to accurately define these parameters for each vehicle class in addition to detailed driving patterns to obtain high-resolution emissions estimates. In this sense, a larger number of vehicle classes included in the model provides more flexibility to develop representative emissions estimates. Emission predictions between modal models were reasonably consistent presenting larger differences with the cycle-variable model, despite both modal models being based on different on-road fleet measurements. In conclusion, analysing emission estimations for different traffic conditions demonstrates the importance of an accurate definition of the model parameters for a specific vehicle fleet.

Application of Different Data Analytics for Evaluation of Heavy Vehicle Vulnerability in Cold-Region Rural Highways

Understanding the impacts of different driving conditions on truck speed is critical to the development and maintenance of resilient highway freight transportation systems. This study attempts to evaluate the combined impact of road weather, travel lane, vehicle type, and truck payload conditions on drivers’ speed choice by modelling speed distributions as normal distributions. Two data analytics, a regression-based approach (RBA) and a central limit theorem (CLT)-based approach (CBA), are adapted to model context-specific speed distributions. The regression-based approach models population-level speed distributions by considering samples of individual speed data, whereas the CBA uses sampling distributions produced according to the CLT. A holistic approach is proposed to identify overall vehicle-specific collision risks imposed by different road-weather conditions, based on the speed distribution parameters estimated. Implications of the study results pertaining to the trucking industry are threefold. First, adapting different data analytics leads to different study results; yet, the CBA is recommended to model speed distributions. Second, truck speeds are significantly affected by the presence of adverse road-weather conditions, yet marginally varied under different loading conditions. Third, overall, tractor–trailer combinations (TTCs) entail high collision risks, particularly when transporting a freight load under adverse road-weather conditions. The study results would be useful to policymakers, particularly for effective speed management in extremely cold regions. Trucking companies may use the study results to identify the least risk-posing road-weather conditions to deploy safe freight transport operations. The resulting speed distribution models are also useful as input to calibrate traffic micro-simulation models.

Evacuation planning for persons with mobility needs: A combined optimization and traffic microsimulation modelling approach

This study presents a framework of optimization and traffic microsimulation modelling that account for a mass evacuation of persons with mobility needs. The study optimizes emergency vehicle and destination allocation and implements an emergency evacuation route within the traffic microsimulation model. The emergency vehicle and destination allocation follow an integer programming optimization approach within a mathematical programming language modelling system. This study tests two evacuation scenarios that consider emergency vehicle operations with and without a dedicated route for the evacuation of five hundred and twelve persons, requiring mobility assistance from six hospitals and nursing homes. Additionally, the evacuation modelling framework considers collision risks in assessing evacuation process, particularly, how it affects evacuation of this group of vulnerable population. Optimization results indicate that this group can be evacuated by ninety emergency vehicles, and it takes 19.5 h for an optimal allocation of emergency vehicles. Traffic simulation results suggest that it takes 21 h to evacuate all persons with mobility needs using ninety emergency vehicles. The study found that travel time along the evacuation route is reduced by 32.31% compared to a sceanrio without a dedicated route. Results also indicate that implementing a dedicated evacuation route yields a reduction of 6 min and 25.2 min in average and the maximum trip time for emergency vehicles, respectively. On the other hand, 68% of emergency vehicles make 0.5–3.5 fewer tour to a case without the collision, leading to an incomplete evacuation (72.2%). This research will provide emergency professionals with insights into the challenges associated with the evacuation of persons with mobility needs and help develop proactive evacuation plans to accommodate the special requirements of the vulnerable population.

Traffic Micro-Simulation-Based Evaluation of Bus Priority With Queue Jump Lane on an Urban Corridor With Heterogeneous Traffic Operations

Queue jump lane (QJL) is a space-optimal facility-based priority measure for faster and smoother movements of buses in urban areas. This study presents an approach to identify the optimal configurations of QJL, bus advance area, and pre-signal for signalized intersections along an urban corridor in India with heterogeneous traffic operations. As this needs to be carried out before field implementation, the traffic impacts of different scenarios were evaluated utilizing a validated VISSIM traffic micro-simulation model. A validated EnViVer emission modeler was used to estimate the vehicular exhaust emissions. The QJL-based priority measures were found to be more effective than a dedicated bus lane along the corridor during the peak hour, which justifies their applicability in cities with restricted road spaces and high traffic demands. To identify the suitable combination of bus priority along the corridor, three additional scenarios were evaluated: bus-optimal, car-optimal, and passenger-optimal. While the passenger-optimal scenario yielded the best impact on travel time performances, the car-optimal scenario was more effective in total emissions performance at the expense of reduced travel time impacts. The conflict between traffic and emissions performances emphasizes the need for investigation of long-term mode and route choice indicators with implementation of bus priority to identify the future mode shares and subsequent impacts. The study was further extended to identify optimal bus stop operations for the baseline and priority scenarios based on multiple combinations of near-side and far-side stops.

Assessing the Impacts of Crowdshipping Using Public Transport: A Case Study in a Middle-Sized Greek City

Crowdsourced deliveries or crowdshipping is identified in recent literature as an emerging urban freight transport solution, aiming at reducing delivery costs, congestion, and environmental impacts. By leveraging the pervasive use of mobile technology, crowdshipping is an emerging solution of the sharing economy in the transport domain, as parcels are delivered by commuters rather than corporations. The objective of this research is to evaluate the impacts of crowdshipping through alternative scenarios that consider various levels of demand and adoption by public transport users who act as crowdshippers, based on a case study example in the city of Volos, Greece. This is achieved through the establishment of a tailored evaluation framework and a city-scale urban freight traffic microsimulation model. Results show that crowdshipping has the potential to mitigate last-mile delivery impacts and effectively contribute to improving the system’s performance.

A dynamic programming optimization for traffic microsimulation modelling of a mass evacuation

• A Framework for using all modes, including transit and school buses in evacuation. • Optimize auto-bus composition for an efficient evacuation. • A case of Halifax is tested with a 5–20% auto-based demand served by buses. • Results from all mode evacuation yield a vehicular traffic reduction of 3.9–7.7%. • An evacuation time reduction of 9–22.7% is achieved in all mode evacuation. This study develops a novel framework to formalize the optimal utilization of all available modes of transportation , particularly transit and school buses for a mass evacuation. The study develops an “All-Mode Evacuation Decision Support Tool (AMEDST)” to determine an optimum auto-bus composition that yields an improvement in evacuation time and network congestion. The study follows a Knapsack optimization and adopts a solution algorithm called Dynamic Programming within a Python platform to optimally allocate buses to evacuees exposed to higher level of vulnerabilities. A traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. Results from the traffic simulation yield a vehicular traffic reduction of 3.9–7.7% and an evacuation time reduction of 9–22.7% if 5–20% of auto-based demand are served by buses. The tool will help emergency personnel evaluate alternative scenarios for making informed decisions regarding resource allocation and emergency budget policies for large-scale evacuations.

Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning

Spatial and traffic planning is important in order to achieve a quality, safe, functional, and integrated urban environment. Different tools and expert models were developed that are aimed at a more objective view of the consequences of reconstruction in different spatial and temporal ranges while respecting selection criteria. In this paper we analyze the application of the multi-criteria analysis method when choosing sustainable traffic solutions in the center of a small town, in this case Belišće, Croatia. The goal of this paper is to examine the possibility of improving the methodology for selecting an optimal spatial–traffic solution by combining the quantifiable results of the traffic microsimulation and the method of multi-criteria optimization. Socially sensitive design should include psychological and social evaluation criteria that are included in this paper as qualitative spatial–urban criteria. In the optimization process, different stakeholder groups (experts, students, and citizens) were actively involved in evaluating the importance of selected criteria. The analysis of stakeholders’ survey results showed statistically significant differences in criteria preference among three groups. The AHP (Analytic Hierarchy Process) multi-criteria analysis method was used; a total of five criteria groups (functional, safety, economic, environmental, and spatial–urban) were developed, which contain 21 criteria and 7 sub-criteria; and the weights of criteria groups were varied based on stakeholders’ preferences. The application of the developed methodology enabled the selection of an optimal solution for the improvement of traffic conditions in a small city with the potential to also be applied to other types of traffic–spatial problems and assure sustainable traffic planning.

Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection

ABSTRACT Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods’ feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case’s traffic emissions.

Improving the variability of urban traffic microsimulation through the calibration of generative parameter models

The calibration of urban traffic microsimulation models is addressed in this paper, focusing on the multivariate distribution of traffic features. This allows to take into account day-to-day variability and possibly complex statistical dependencies, enabling robust validation and the computation of more accurate statistics. Variability in traffic models can be obtained, in principle, by suitably tuning origin-destination demand flows, but this becomes problematic for microsimulation models when complex multivariate distributions are considered, due to excessive dimensionality and nonlinearity. Thus, here we investigate how complex multivariate behavior can be achieved by suitably varying only a subset of the parameters ruling the microsimulation dynamics, without imposing any distribution on the demand flows. To this purpose, rather than looking for fixed values of the parameters, we exploit distribution models having the structure of mixtures of uniforms, through which the simulator parameter values can be randomly sampled, thus replacing the simulator inner randomization mechanism. We formalize the optimization of the mixture parameters through a maximum mean discrepancy principle involving the simulator dynamics, which leads to a large-dimensional non-differentiable problem that we solve through a method based on cross-entropy. Preliminary experiments concerning the application of the proposed methodology to the SUMO simulator show how it is possible to capture quite complex multivariate distributions of target flows varying only four parameters.

Mass evacuation microsimulation modeling considering traffic disruptions

This study presents a framework of traffic evacuation microsimulation modeling that accounts for uncertain network disruptions endogenous to traffic operations. While evacuation modeling considers external stresses such as flooding-related network disruptions, the risks inherent to the transport operations, particularly vehicle collisions may also cause disruptions to evacuation traffic flows. This study adopts a combined Bayes theory and Monte Carlo simulation approach to identify collision hotspots and their occurrence over different times of an evacuation day. A traffic evacuation microsimulation model is developed which explicitly incorporates vehicle collision-related disruptions at the hotspots identified by this probabilistic model. The proposed probabilistic approach identifies 128 candidate collision locations within the study area. The probabilities of candidate locations to anticipate a vehicle collision range between 0.21 and 7.0%. Based on the probabilities, the Monte Carlo simulation approach identifies five hotspots for traffic microsimulation modeling of vehicle collisions during the evacuation. The results from the traffic simulation reveal that due to concurrent collision occurrence, evacuation times vary within 23–31 h depending on the time required to remove traffic disruptions from the network. On the other hand, the concurrent collision occurrence at the hotspots increases the complete evacuation time by almost 11 h if the disruption is not removed from the network, an increase of 50%, compared to an evacuation scenario without disruptions. The analysis of simulated queue length reveals that the hotspots’ traffic queues range from 0.28 to 2.06 km depending on their locations in the study area. The study asserts that an evacuation model without the consideration of the network disruptions due to endogenous risks may underestimate the traffic impacts and network clearance time for an evacuation. These results will provide emergency professionals with insights into managing emergency traffic operation subjected to uncertainties.

Shelter locations in evacuation: A Multiple Criteria Evaluation combined with flood risk and traffic microsimulation modeling

This study presents a framework for systematic identification and evaluation of potential shelter locations for a mass evacuation due to a hurricane and its associated flooding. The sequential process involves identifying candidate facilities for use as shelters, developing a site selection process for shelter locations, and applying the site selection process to evaluate candidates. The shelter site selection process follows three stages: candidate-selection, candidate-screening, and candidate-evaluation using a Multiple Criteria Evaluation (MCE). The criteria fall into four categories: Environmental Considerations, Structural Attributes, Access to Emergency Services, and Transportation Considerations. A combined flood risk and traffic microsimulation model informs the MCE with information regarding the environmental and traffic flow-related impacts, including traffic congestion. Six weight schemes are tested, one of which is based on expert consultation. The results reveal that candidates that score the highest according to the MCE are approximately 3–20 km away from the affected area, and the lowest scoring candidates are in distant rural areas immediately adjacent to the coast. The scoring scheme emphasizing on transportation considerations suggest that some candidates that are closer to the Peninsula score higher. Four of the top five highest scoring candidates are among those that had been identified by stakeholders. Recommendations are made for shelters to be used in time-pressing evacuations, shelters that have a high-capacity, and shelters that are municipally owned. The proposed method in this study can be used in future research on evacuation planning, particularly in relation to traffic modeling as well as by emergency professionals.

Dynamic Evaluation of Traffic Noise through Standard and Multifractal Models

Traffic microsimulation models use the movement of individual driver-vehicle-units (DVUs) and their interactions, which allows a detailed estimation of the traffic noise using Common Noise Assessment Methods (CNOSSOS). The Dynamic Traffic Noise Assessment (DTNA) methodology is applied to real traffic situations, then compared to on-field noise levels from measurement campaigns. This makes it possible to determine the influence of certain local traffic factors on the evaluation of noise. The pattern of distribution of vehicles along the avenue is related to the logic of traffic light control. The analysis of the inter-cycles noise variability during the simulation and measurement time shows no influence from local factors on the prediction of the dynamic traffic noise assessment tool based on CNOSSOS. A multifractal approach of acoustic waves propagation and the source behaviors in the traffic area are implemented. The novelty of the approach also comes from the multifractal model’s freedom which allows the simulation, through the fractality degree, of various behaviors of the acoustic waves. The mathematical backbone of the model is developed on Cayley–Klein-type absolute geometries, implying harmonic mappings between the usual space and the Lobacevsky plane in a Poincaré metric. The isomorphism of two groups of SL(2R) type showcases joint invariant functions that allow associations of pulsations–velocities manifolds type.

Left turn crash risk analysis: Development of a microsimulation modeling approach

The use of traffic simulation to analyze complex transportation issues has become common practice in transportation engineering. The further application of microsimulation to the analysis of traffic safety in a systematic, rigorous, and controlled fashion is becoming increasingly viable as simulation models improve and tools for quantifying surrogate safety measures become readily accessible. Using a calibrated traffic microsimulation model and surrogate safety assessment model analysis, this paper examined how the risk for left-turn crashes varied as traffic conditions changed at a signalized intersection.Safety impacts for 750 unique combinations of intersection geometry, traffic, and signal timing parameters were simulated and the number of left-turn conflicts per hour noted. Results of the simulation analyses were used to develop statistical models that expressed the risk of occurrence of a left-turn crash during a given hour as a function of the left-turn phasing mode and prevailing conditions during that hour. The study was motivated by the recent widespread application of the flashing yellow arrow (FYA) which provides the opportunity to vary left-turn phasing mode by time of day—potentially leading to more efficient traffic operations at signalized intersections. In this regard, the study addresses a basic need for tools that predict how the risk for left-turn crashes might vary at a more disaggregated level than that provided by existing crash prediction models, which typically predict yearly totals of left-turn crashes, often based on annual average daily traffic volumes. Potential application of the model to the implementation of a time-variable safety-based left-turn phasing selection scheme using FYA was successfully demonstrated.