A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method

The inherent intention and decision process of lane changes are complex and unobservable. Though the external environment and traffic conditions are changing along the traveling direction, the drivers’ characteristics and preferences may lead to persistence of preferable lane choices. Hidden Markov Model (HMM) method is used to model the system that involves unobservable factors, such as speech recognition and biological sequence problems. The hidden process are assumed to associate with observable outcomes. In this study, HMM is integrated into a two-stage lane-changing model to better represent the mandatory lane-changing behaviors on arterials. The lane-changing decision process is separated into two steps: decision to target a lane as the desire lane and acceptance of available gaps in the chosen direction. The outcome of the first step is unobservable and treated as the latent state in HMM. The second step, gap acceptance model, relates the outcome of the first step to observed vehicle trajectories. The proposed model is estimated and validated using detail Next Generation Simulation (NGSIM) vehicle trajectory data from Lankershim Boulevard. Comparison between generated lane position sequences and original trajectories validated the model’s capability of representing mandatory lane changes. There is an average 17% difference on predicted lane change locations compared to observed locations; while lane change locations to left turn lane and right turn lane show 10% and 13% difference respectively. The generated lane changes show a late tendency of movements among through lanes. The results show that the model is fit for the purpose of representing mandatory lane change behaviors on arterials. The research highlights some future improvements of proposed lane-changing model on arterials

Results of Micro-Simulation Model for Exploring Drivers' Behavior on Acceleration Lanes

This study examines drivers' behavior on acceleration lanes, close to the convergence between the main and the secondary traffic streams, by means of traffic micro-simulations. Experimental data collected videotaping two acceleration lanes in Italy have been used to initially calibrate a simulation model and to validate it subsequently. The analyses had focused on both vehicles coming from the on-ramp, in terms of entrance points dispersion into the main traffic stream along the acceleration lanes, merging speeds, and acceleration rates reached, and on vehicles driving on the freeway right lane, in terms of vehicles categories, traffic volumes, and speeds. The maneuvers have been implemented in the TransModeler traffic simulation package and several scenarios have been considered, changing the traffic composition and the speeds at which drivers enter the acceleration lane from time to time. This led to obtain a large number of case studies, where the mutual influence between the two flows combined with the vehicle performances and the psychophysical characteristics of drivers, have led to an initial evaluation of the main variables in respect of which the required length for the specialized lanes depends. Road design guidelines' standards have been later compared to what was observed in reality and it can be claimed that the microscopic traffic model in some cases confirms the standards of road design guidelines while, in other cases, contradicts them

Safety Evaluation of Car-Truck Mixed Traffic Flow on Freeways Using Surrogate Safety Measures

This study analyzes car-following and lane-change conflicts in car-heavy vehicle mixed traffic flow on freeways using three surrogate safety measures - time-to-collision (TTC), post-encroachment-time (PET) and crash potential index (CPI). The surrogate safety measures were estimated for different types of lead and following vehicles (car or heavy vehicle) using the individual vehicle trajectory data. The data were collected from a segment of the US-101 freeway in Los Angeles, California, U.S.A. For car-following conflicts, the distributions of TTC and PET were significantly different among different types of lead and following vehicles. For lane-change conflicts between the lane-change vehicle and the trailing vehicle in the target lane, CPIs were higher for angle conflicts than rear-end conflicts. It was also found that the CPI was generally higher for a given spacing interval when the following vehicle is a heavy vehicle in both car-following and lane-change conflicts. This indicates that heavy vehicle’s lower braking capability significantly increases collision risk. This study also validates the CPI using historical crash data and the loop detector data extracted a few minutes before crash time upstream and downstream of crash locations. The data were obtained from a section of the Gardiner Expressway, Ontario, Canada. The result shows that the values of CPI were consistently higher for the crash case than the non-crash case. This shows that CPI can be used to capture the collision risk during car-following and lane-change maneuver on freeways. The findings suggest that the differences in collision risk among different vehicle pair types should be considered in the assessment of safety of car-heavy vehicle mixed traffic flow

Simulating the Impact of Traffic Calming Strategies

This study assessed the impact of traffic calming measures to the speed, travel times and capacity of residential roadways. The study focused on two types of speed tables, speed humps and a raised crosswalk. A moving test vehicle equipped with GPS receivers that allowed calculation of speeds and determination of speed profiles at 1s intervals were used. Multi-regime model was used to provide the best fit using steady state equations; hence the corresponding speed-flow relationships were established for different calming scenarios. It was found that capacities of residential roadway segments due to presence of calming features ranged from 640 to 730 vph. However, the capacity varied with the spacing of the calming features in which spacing speed tables at 1050 ft apart caused a 23% reduction in capacity while 350-ft spacing reduced capacity by 32%. Analysis showed a linear decrease of capacity of approximately 20 vphpl, 37 vphpl and 34 vphpl when 17 ft wide speed tables were spaced at 350 ft, 700 ft, and 1050 ft apart respectively. For speed hump calming features, spacing humps at 350 ft reduced capacity by about 33% while a 700 ft spacing reduced capacity by 30%. The study concludes that speed tables are slightly better than speed humps in terms of preserving the roadway capacity. Also, traffic calming measures significantly reduce the speeds of vehicles, and it is best to keep spacing of 630 ft or less to achieve desirable crossing speeds of less or equal to 15 mph especially in a street with schools nearby. A microscopic simulation model was developed to replicate the driving behavior of traffic on urban road diets roads to analyze the influence of bus stops on traffic flow and safety. The impacts of safety were assessed using surrogate measures of safety (SSAM). The study found that presence of a bus stops for 10, 20 and 30 s dwell times have almost 9.5%, 12%, and 20% effect on traffic speed reductions when 300 veh/hr flow is considered. A comparison of reduction in speed of traffic on an 11 ft wide road lane of a road diet due to curbside stops and bus bays for a mean of 30s with a standard deviation of 5s dwell time case was conducted. Results showed that a bus stop bay with the stated bus dwell time causes an approximate 8% speed reduction to traffic at a flow level of about 1400 vph. Analysis of the trajectories from bust stop locations showed that at 0, 25, 50, 75, 100, 125, 150, and 175 feet from the intersection the number of conflicts is affected by the presence and location of a curbside stop on a segment with a road diet

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers’ natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies

Driving Etiquette

Technical ReportEstablish driving etiquette based on naturalistic driving behavior of human drivers to serve as the basisfor the design of autonomous vehicles to drive "like safe human drivers." This project queried a largeamount of naturalistic driving data from the Ann Arbor connected vehicle deployment. The data wereused to train algorithms to learn about "what is appropriate" based on statistical analysis of humandriving behaviors.United States Department of Transportationhttps://deepblue.lib.umich.edu/bitstream/2027.42/156052/4/Driving Etiquette.pdfDescription of Driving Etiquette.pdf : Final Repor

A stochastic mesoscopic cell-transmission model for operational analysis of large-scale transportation networks

The cell transmission model (CTM), developed by Daganzo in 1994 was not fully exploited as an operations model for analysis of large-scale traffic networks. Because of its macroscopic / mesoscopic features, CTM offers calibration and computational advantages over microscopic models. This study presents a series of enhancements to the original form of CTM. These enhancements show potential to increase the model’s accuracy and realism of traffic flow representation. For example, topological enhancements and modifications to the flow advancing equation are introduced to allow variable cell lengths and non-discrete movements of vehicles between cells. In addition, implementation of lane-changing behavioral logics and algorithmic enhancements to model vehicle flows at network junctions demonstrate potential in modeling realistic non-homogeneous traffic streams in CTM. A calibration exercise was conducted to account for randomness in driving behavior using vehicle trajectory data. This proves the models potential in modeling stochastic variations of real-life networks. A sample freeway network of I-10 corridor in Baton Rouge was used to evaluate and compare the performance of the improved version of CTM versus CORSIM. The simulation results showed comparable performance of both platforms in terms of link occupancy (density) and total network travel time and demonstrate the potential of employing CTM in traffic operations applications