Measuring working memory load effects on electrophysiological markers of attention orienting during a simulated drive

Intersection accidents result in a significant proportion of road fatalities, and attention allocation likely plays a role. Attention allocation may depend on (limited) working memory (WM) capacity. Driving is often combined with tasks increasing WM load, consequently impairing attention orienting. This study (n = 22) investigated WM load effects on event-related potentials (ERPs) related to attention orienting. A simulated driving environment allowed continuous lane-keeping measurement. Participants were asked to orient attention covertly towards the side indicated by an arrow, and to respond only to moving cars appearing on the attended side by pressing a button. WM load was manipulated using a concurrent memory task. ERPs showed typical attentional modulation (cue: contralateral negativity, LDAP; car: N1, P1, SN and P3) under low and high load conditions. With increased WM load, lane-keeping performance improved, while dual task performance degraded (memory task: increased error rate; orienting task: increased false alarms, smaller P3). Practitioner Summary: Intersection driver-support systems aim to improve traffic safety and flow. However, in-vehicle systems induce WM load, increasing the tendency to yield. Traffic flow reduces if drivers stop at inappropriate times, reducing the effectiveness of systems. Consequently, driver-support systems could include WM load measurement during driving in the development phase

Computational interaction models for automated vehicles and cyclists

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerableroad users because they are not protected by a physical compartment around them. In recentyears, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists hasactually increased. Most of the conflicts between cyclists and motorized vehicles occur atcrossings where they cross each other’s path. Automated vehicles (AVs) are being developedto increase traffic safety and reduce human errors in driving tasks, including when theyencounter cyclists at intersections. AVs use behavioral models to predict other road user’sbehaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclistsinteract and communicate with motorized vehicles at conflicting scenarios like unsignalizedintersections. This understanding will be used to develop accurate computational models ofcyclists’ behavior when they interact with motorized vehicles in conflict scenarios.The overall goal of this thesis is to investigate how cyclists communicate and interact withmotorized vehicles in the specific conflict scenario of an unsignalized intersection. In the firstof two studies, naturalistic data was used to model the cyclists’ decision whether to yield to apassenger car at an unsignalized intersection. Interaction events were extracted from thetrajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Bothcyclists’ kinematics and visual cues were found to be significant in predicting who crossed theintersection first. The second study used a cycling simulator to acquire in-depth knowledgeabout cyclists’ behavioral patterns as they interacted with an approaching vehicle at theunsignalized intersection. Two independent variables were manipulated across the trials:difference in time to arrival at the intersection (DTA) and visibility condition (field of viewdistance). Results from the mixed effect logistic model showed that only DTA affected thecyclist’s decision to cross before the vehicle. However, increasing the visibility at theintersection reduced the severity of the cyclists’ braking profiles. Both studies contributed tothe development of computational models of cyclist behavior that may be used to support safeautomated driving.Future work aims to find differences in cyclists’ interactions with different vehicle types, suchas passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluatedfrom the driver’s perspective by using a driving simulator instead of a riding simulator. Thissetup would allow us to investigate how drivers respond to cyclists at the same intersection.The resulting data will contribute to the development of accurate predictive models for AVs

Network-Wide Pedestrian and Bicycle Crash Analysis with Statistical and Machine Learning Models in Utah

Recent trends in crashes indicate a dramatic increase in both the number and share of pedestrian and bicyclist injuries and fatalities nationally and in many states. Crash frequency modeling was undertaken to identify crash prone characteristics of segments and non-signalized intersections and explore possible non-linear associations of explanatory variables with crashes. Crowdsourced “Strava” app data was used for bicycle volume, and pedestrian counts estimated from nearby signalized intersections were used as pedestrian volume. Multiple negative binomial models investigated crashes at different spatial scales to account for different levels of data availability and completeness. The models showed high traffic volume, steeper vertical grades on roads, frequent bus and rail stations, greater driveway density, more legs at intersections, streets with high large truck presence, greater residential and employment density, as a larger share of low-income households and non-white race/ethnicity groups are indicators of locations with more pedestrian and bicycle crashes. Crash severity model results showed that crashes occurring at mid-blocks and near vertical grades were more severe compared to crashes at intersections. High daily temperature, driving under influence, and distracted driving also increases injury severity in crashes. This study suggests potential countermeasures, policy implications, and the scope of future research for improving pedestrian and bicycle safety at segments and at non-signalized intersections

An empirical delay model for application in unsignalized intersections in dynamic traffic assignment

textUp until recently, unsignalized nodes have been either ignored or inadequately represented in Dynamic Traffic Assignment (DTA) models. This is due to the difficult nature of incorporating internal node conflicts into dynamic flow models. It was thought or assumed that these nodes had little impact on overall model results, but evidence from testing in Visual Interactive System for Transportation Algorithms (VISTA), a DTA model, reveals that may not be the case. This paper explores recent attempts at characterizing stop sign effects within DTA flow models. From previous studies, it has been found that incorporating these unsignalized and priority movements internal to the flow model requires large amounts of computational power, are challenging to make efficient, and lead to a multiple or infinite solution space. Based on these findings, a deterministic approach is both impractical and likely impossible in the existing framework of the Cell Transmission (CTM) and Link Transmission (LTM) models commonly used in DTA. Thus, a method of utilizing empirical relationships based on information readily available in these models may be a more acceptable approach. Microsimulation is much more suitable for modeling these types of interactions and is capable of producing results near to reality. For this reason, microsimulation was chosen as a viable method for developing empirical relationships of such complex interactions to then be used as inputs into the macroscopic flow models of DTA. This paper presents a model developed to calculate delays expected by vehicles at stop approaches based on information that can be taken from a dynamic flow model such as CTM and LTM models. This model is validated by video data recorded and analyzed for accuracy. Potential uses and probable implementations of the model are explored to appropriately incorporate unsignalized and priority movements into existing flow models.Civil, Architectural, and Environmental Engineerin

Examining the safety performance of urban/suburban arterials and freeway segments in consideration of roadway geometry and traffic control

With the sprawling of major cities and creation of suburban areas, one problem that state agencies face is the increasing congestion in suburban arterials coupled with the safety risks posed by increasing traffic volumes at traditional intersections along arterials. In the early 1960s, a new intersection design was developed and installed in the state of Michigan, where left turns at intersections were replaced by median U-turn lanes (MUTs). This study focuses on the safety performance of corridors where median U-turns (MUTs) are present along urban and suburban boulevards. The analysis is performed in two stages; first models were developed separately for assessing the safety performance, through the examination of crash frequency and type, across individual MUTs, at intersections, and along segments on which MUTs are located. Subsequently, an aggregate-level analysis is conducted to assess the safety performance of specific intersections/MUT combinations. The second stage focused on developing models for examination of sites spanning each side of an intersection including upstream and downstream MUTs. These sites were compared to sample sites with allowed traditional left turn movements. Ultimately, the results provide guidance to agencies considering the installation of such alternative intersections. Additionally, safety risks are present during work zone projects along freeways, which are essential facilities for providing mobility. The presence of a work zone generally results in both mobility and safety impacts to road users. Minimizing the adverse impacts associated with work zones has become a priority for road agencies. This study will estimate SPFs that consider freeway geometry and traffic conditions, as well as the effects of various temporary traffic control strategies such as lane shifts, shoulder closures, and lane closures. Crash modification factors were developed for work zone duration and length. Additionally, the study results provide insight on the safety impacts associated with each of the four types of lane closures

Comparative Data Analysis of Older Driver's vs Younger Driver's Gap Acceptance Behavior at signalized left turns - A driving Simulator Study

Drivers aged 65 and older are particularly prone to motor vehicle crashes, with approximately 20% of traffic fatalities occurring at intersections [11]. Intersections appear to be hazardous for drivers in this age group due to cognitive, perceptual, and psychomotor challenges. Older drivers find it particularly difficult to safely navigate left turns at signalized permissive intersections, having problems adequately detecting, perceiving, and accurately judging the safety of gaps. The increase in the number of elderly drivers has been paralleled by an increase in road-related accidents due to age-related fragility. By 2030, more than 21% of the adult population is projected to be over 65 years old [1]. However, previous studies have not adequately considered the combined effects of the randomized gap, queue length, traffic volume, pedestrians, and physiological factors on driving. The current study aims to address the gap in the literature by explicitly examining older and younger drivers’ gap acceptance behaviors during permissive left turns at four-way intersections. The main objective of this thesis is to study, identify and analyze the effect of Gap Acceptance Behavior on age, traffic volume, queue length, and physiological factors such as heart rate variability (HRV), electrodermal activity (EDA), and motion sickness among older and younger drivers. The data was collected from a driving simulator study comprising 40 participants aged between 20-30 for younger and 65 years for older. The collected data was used for comparative analysis, with the Gap Accepted by the drivers calculated from the video data. The gap is calculated as the distance between the left turning vehicle and the oncoming traffic. All recruited drivers were healthy. Each participant navigated twelve scenarios, six with lower traffic conditions and six with higher traffic conditions. Each lower and higher traffic scenario varied in queue length, with the number of cars in front of the ego vehicle varying from 0, 1, and 2. All varying queue lengths also had one with a pedestrian and another without. The physiological data collected through the Empatica4 wristband was also considered to study the gap acceptance behavior. Another parameter, motion sickness susceptibility score (MSSQ), was obtained from a questionnaire the participants completed after the experiment. Of these factors, queue length, traffic volume, and pedestrians play a significant role in studying gap acceptance. There is a significant difference in accepting and rejecting the gap between young and older drivers. Older drivers’ decision is affected more by factors, such as traffic volume, age, queue length, HRV, EDA, MSSQ score and the presence of pedestrians. This study showed that older drivers exhibited longer gap acceptance times than their younger counterparts while turning left across traffic at permissive intersections. Researchers may use the findings to better understand gap acceptance behaviors, while policymakers may utilize the results to design mobility guidelines

ASSESSING THE IMPACT OF BICYCLE TREATMENTS ON BICYCLE SAFETY: A MULTI-METHODS APPROACH

Compared to other modes, bicyclists are disproportionally affected by crashes considering their low mode share. There is evidence that crashes between bicyclists and motorized vehicle take place at road segments and signalized intersections where bicycle treatments (e.g., bike lanes) are present, urging for in-dept analysis of the safety impact of the various bicycle treatment types. Additionally, it is important to identify sensor types that have the potential to advance field data collection and traffic monitoring in multi-modal road environments. In this dissertation, three approaches, namely crash analysis, traffic conflict analysis, and analysis of driver speeding and glancing behavior, were implemented to investigate the safety impact of bicycle treatments at the segment- and the intersection-levels on bicycle safety. Prediction models were developed to predict bicycle-motorized vehicle crashes at road segments and signalized intersections, and traffic conflicts between straight-going bicyclists and right-turning vehicles at signalized intersections. Driver speeding and glancing behavior was analysed for the segment and the intersection levels. A mode classification framework to classify trajectories recorded using a radar-based sensor was developed to test the feasibility of using radar-based sensors in field studies. The findings of this dissertation contribute to bicycle safety research in terms of quantifying the safety impact of various bicycle treatment types and how to assess and also, by showing how to assess bicycle safety. The findings of this research have the potential to stand as a valuable tool for transportation policymakers and officials in charge of establishing safe bicycle networks

Evaluation of Unconventional Signalized Intersections on Arterial Roads and a Proposition for a Novel Intersection Design

Several unconventional intersection designs were proposed and implemented to enhance traffic safety and operation at intersections. The efficiency of these intersection designs was not sufficiently evaluated in the previous research because of the limited implementation of such designs. However, with the growing interest in the implementation of unconventional intersections by municipalities and transport agencies, it has become a need for a comprehensive evaluation of their safety and operational benefits. Therefore, this dissertation aims to evaluate the safety and operational aspects of unconventional intersection designs by employing different research approaches: crash analysis, microscopic simulation, and driving simulation. Firstly, this dissertation evaluated the effectiveness of median U-turn crossover-based intersections (median U-turn (MUT) and restricted crossing U-turn (RCUT) intersections), which have the least number of traffic conflicts among other unconventional intersection designs, in enhancing traffic safety by estimating crash modification factors (CMF) for their implementation. The results indicated that MUT and RCUT intersections are safer than the 4-leg conventional intersection. Secondly, A new innovative intersection design, which has been given the name Shifting Movements (SM) intersection, was introduced and proposed to replace the implementation of the RCUT intersection under moderate and heavy minor road traffic conditions. Evaluation of the operational benefits of this intersection design was performed in the microscopic simulation environment by assuming different traffic volume levels and left-turn proportions to represent the peak hour with moderate to high left-turn traffic volumes. The results demonstrated that the SM intersection design significantly outperforms conventional and RCUT intersections when they are subjected to high traffic volumes in terms of average control delay and throughput. Finally, A driving simulation experiment was conducted to evaluate the safety aspects of the SM intersection design. Several surrogate safety measures were adopted for the evaluation. The effectiveness of using infrastructure-to-vehicle (I2V) communication for mitigating the confusion at unconventional intersections has been also evaluated in this research. Findings indicated that RCUT and SM intersections have similar safety performance and crossing them is safer than crossing the 4-leg conventional intersection. It was found that using I2V communication is helpful in understanding unconventional movement patterns. This dissertation can be a solid reference for decision-makers regarding the implementation of unconventional intersection designs

Safety and Operational Impacts of Alternative Intersections

As the degradation of service at some conventional intersections increases, there becomes a need for alternative solutions other than expensive interchanges. Many alternative intersections have been proposed in the past. Under certain traffic and local conditions some solutions are more promising than other. In some cases, the conventional intersection may still be the optimal choice. The presented research focused on developing guidelines that would help planners and designers identify the most promising solutions for further analysis. This objective has been addresses in two ways. Firstly, the existing knowledge on alternative intersections has been identified. Secondly, the performance of conventional and alternative intersections under a range of Indiana traffic conditions has been evaluated using micro-simulation model - VISSIM. Although a large number of sources could be found on the research subject, the existing knowledge about performance of alternative intersection design is incomplete. Only a few designs proposed in the past have been applied at a considerable number of locations including roundabouts, median U-turns, and jag-handle intersections. Other types still await implementation. The available sources are not comprehensive and deal with conditions that might be different from Indiana. The knowledge of the safety impact of these intersections is very limited. A large number of more than 1,300 scenarios were simulated runs performed with VISSIM calibrated to Indiana conditions. The simulated types of intersections included: conventional, roundabouts, jag-handle near-sided and far-sided, median U-turns, and continuous-flow intersection. Except roundabouts, all other intersections were signalized to test their capacity limits and delay-based performance. Although the roundabouts were the lowest delays at low volumes they also reached the capacity before other did. The most promising solutions for heavy volumes are median U-turns and continuous-flow intersections. The presented research developed guidelines for using alternative intersection designs. The guidelines compile the existing knowledge found in existing publications and research reports with the simulation experiments performed with VISSIM. The guidelines are ready to use and will help planners and designers determine which intersection types are the most promising under considered conditions and should be considered in a detailed way. The simulation results have been summarized in an easy to use format of graphs