Intersection Types Research Articles

Approaching intersections: Gaze behavior of drivers depending on traffic, intersection type, driving maneuver, and secondary task involvement

Urban intersections are hotspots for crashes because they provide a location for several traffic streams and types of road users to cross. A main cause of crashes is the misinformation of drivers as they fail to sense relevant visual information. We aimed to analyze the gaze behavior of car drivers in a variety of intersection scenarios, bringing together the partial findings of previous research, and examine the interdependencies of the contributing factors to provide a database for driver modeling. In a driving simulator study with 59 participants, we varied intersection scenarios regarding drivers’ right of way (yield sign, green traffic light), intersection type (T junction, X intersection), surrounding traffic (none, irrelevant, relevant), and intended driving maneuver (left turn, right turn, going straight). A total of 25 intersection scenarios were presented in a within-subjects design to a control group and a group with a cognitive load task (counting back in numbers of two). Fixations were coded regarding defined areas of interest in the field of view and separated according to three segments of the intersection approach: 75–50 m, 50–25 m, and 25–0 m before entering the intersection. The results show that the effect of surrounding traffic, secondary task engagement, and the intended driving maneuver changed dramatically depending on the right of way of the driver. Surrounding traffic primarily affected gaze behavior in scenarios of ceding the right of way close to the intersection entry. The cognitive load task increased fixations on the road center especially in situations where the driver had the right of way, but less in situations of ceding the right of way. Interactions with the type of intersection were only apparent for different driving maneuvers. This study provides a detailed and comprehensive picture of drivers’ attentional processes when approaching intersections which is relevant for understanding and modeling of driver behavior in urban traffic.

A Bayesian Approach to Examine the Impact of Pavement Friction on Intersection Safety

The safety of intersections has been the focus of many studies since intersections are considered hazardous zones of road networks. Identifying the main contributing factors of severe traffic crashes at intersections is crucial to implementing appropriate countermeasures. We investigated the major contributing factors to crash injury severity at intersections, particularly pavement surface friction. Nine years of intersection crash data in Wyoming have been analyzed for this study. The random forest technique was employed to identify the importance of critical variables influencing crash injury severity risk. Subsequently, a Bayesian ordinal probit model was applied to examine the relationships between crash injury severity risk and these crash contributing factors. As per the random forest model’s results, pavement friction has a strong impact on crash injury severity risk along with using safety restraints, intersection type, signalized or unsignalized, reckless driving, and crash type. The results of the Bayesian model demonstrated that higher pavement surface friction levels and proper use of restraints reduced the likelihood of severe injury. Based on these findings, several countermeasures may be proposed, such as those pavement friction requirements, driver’s education, and traffic law enforcement to mitigate injury severity concerns at intersections.

Increasing the capacity of the unsignalized three way Petir Intersection, Serang Regency

<p>Population growth increases every year, especially in Serang Regency, causing traffic problems including accidents, congestion, or being faced with conditions of frequent road scrambling. In preliminary observations, this happened to the Unsignalized Three Way Petir Intersection . This research was conducted to determine the existing performance of the intersection and find alternative solutions to overcome the problem situation and find out the cost needs needed in planning the proposal. Thus, of course, data is needed to be able to analyze it. The method used is based on the Indonesian Road Capacity Manual (MKJI) of 1997, the required data consists of primary data by conducting a field survey, to find out the volume of vehicles (Average daily traffic / ADT for seven days from 06.00 to 18.00 WIB, road geometrics, traffic conditions, and environmental conditions. It is known that the Unsignalized Three Way Petir Intersection type is type 322, meaning 3 arms 2 lanes 2 directions, the data uses the form USIG I and the form USIG II from MKJI 1997. The results showed that the existing performance at the Unsignalized Three Way Petir Intersection showed the largest total traffic flow value of 3,480 passenger car unit (pcu) / hour, the saturation degree (DS) value was 1,026, which means, the traffic flow has exceeded the road capacity, with an intersection traffic delay of 16,426 sec / pcu, a delay in main road traffic (B-D) of 7.4584 sec / pcu and a delay in minor road traffic (C) of 38.2352 sec / pcu, queue odds are between 42.3554% to 84%. Alternative solutions that can be done prohibit the right turn traffic from minor roads, as well as widening on the main road from 3 m per lane to 3.5 m so that the short width of each lane = 3.5 m and the shoulder width of 0.5 m, in total to 8 m, so that the cost required in the planning is Rp. 2,220,051,000 (two billion two hundred twenty million fifty-one thousand rupiah).</p>

On sets of terms having a given intersection type

Working in a variant of the intersection type assignment system of Coppo, Dezani-Ciancaglini and Venneri [1981], we prove several facts about sets of terms having a given intersection type. Our main result is that every strongly normalizing term M admits a *uniqueness typing*, which is a pair $(\Gamma,A)$ such that 1) $\Gamma \vdash M : A$ 2) $\Gamma \vdash N : A \Longrightarrow M =_{\beta\eta} N$ We also discuss several presentations of intersection type algebras, and the corresponding choices of type assignment rules. Moreover, we show that the set of closed terms with a given type is uniformly separable, and, if infinite, forms an adequate numeral system. The proof of this fact uses an internal version of the B\"ohm-out technique, adapted to terms of a given intersection type.

Transportation-Related Emissions Surrogates Support Modeling of Oxides of Nitrogen Exposure

Background: Mobile sources such as traffic and airports contribute significantly to concentrations of nitrogen oxides (NOx) and nitrogen dioxide (NO2). Exposure to these pollutants in turn are associated with a range of negative health outcomes. Accurate, high resolution spatiotemporal models of NOx /NO2 are needed to support further epidemiological research. Traffic tailpipe emissions are especially elevated under high engine load (acceleration), and our earlier work shows accounting for airport emissions may decrease measurement error in our spatiotemporal NOx models. The goal of this work is to showcase improvements in accounting for these mobile sources more accurately and these features’ importance in our spatiotemporal NOx models. Methods: We calculated airport-related NOx emissions by allocating airport-related NOx emissions from CARB’s CEPAM emission inventory tool to each airport in California from 2004 through 2019. For 2020 and 2021, emissions were further scaled on a weekly basis to account for decreased flight volume during the COVID period. To account for on-road vehicle tailpipe emissions due to acceleration at intersections, we developed a dataset representing intersection density, broken into class by intersection type based on road class type and supported by GPS activity data. These variables were incorporated into spatiotemporal modeling of outdoor NOx/NO2 concentrations in California. Results: Airport emissions and intersection density were ranked highly by variable importance within the model. The transportation-related emissions surrogates improved the spatial representation of NOx/NO2 at fine scales, supporting accurate air quality exposure estimates. Conclusions: Our findings demonstrate how improved measures of mobile source emissions (beyond distance to airports or roads) that capture gradients in emission patterns across spatial features (e.g. road class) can enhance spatiotemporal prediction performance of high resolution exposure models. Keywords: Air pollution, NOx, transportation, mobile sources

Longitudinal Driving Behavior before, during, and after a Left-Turn Movement at Signalized Intersections: A Naturalistic Driving Study in China

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that “the closer to the stop line, the higher the variation of acceleration”. Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles.

Detecting and learning city intersection traffic contexts for autonomous vehicles

According to Business Wire, the global market for Autonomous Vehicles estimated at 6.1 thousand Units fpsin the year 2020, is projected to reach a revised size of 110.1 thousand Units by 2026, growing at a CAGR of 60.6% over the analysis period. This strong demand brings more research interests in autonomous vehicle systems. One of the hot topics is autonomous machine vision system and intelligent solutions. Most existing papers on autonomous vehicle machine vision systems apply machine learning models to address automatic object detection and classification issues to support automatic street traffic object detection and classification for vehicles, people/animal, traffic road signs, and signals. However, there is a lack of research results addressing automatic detection and classification of road contexts and transportation intersections under diverse weather conditions. In this work, we present an integrated machine learning model to address the issue and need in street intersection detection and classification based on road contexts and weather conditions. This paper reports our efforts in data collection, processing, and training based on existing data sets (such as BDD100k and COCO), and add a new training data set on street contexts and intersections (13 classes) and weather conditions (6 classes). This paper proposes a 2-stage integrated model to support the detection and classification of different types of traffic contexts and transportation intersection. In the first stage, two deep learning models (Yolo4 and Mask CNN) with transfer learning technique are used to detect the traffic signs, traffic lights, crosswalk and road direction targets on the road in addition to mobile objects (such as people and cars). Later, the generated results from the first stage are used as inputs to a decision tree model to detect and classify the different types of underlying transportation intersections. According to presented experimental results, the proposed 2-stage model receives a high accuracy, so it has strong potential application in computer vision technology of autonomous driving.

Comprehensive Evaluation of Operational Efficiency of Intersections in Arterial Considering Pedestrians Yield Rule

Yield to pedestrians has become a new trend of civilized transportation in the metropolis. In order to evaluate the influence of yield behavior on the comprehensive operation efficiency of signalized intersections in arterial, the efficiency of the subject who gives way and the subject who is given up in the process of yielding to pedestrians was considered in this study, and the comprehensive operation evaluation of intersection in arterial was given. First, based on the rule of pedestrians yield, the concept of a safe headway gap of pedestrians was introduced in the process of conflict analysis of traffic flows at the intersection, and 3 situations were discussed, which are no yield, yield to 1 flow and 2 flows, to calculate the departure rate of traffic flow at the intersection. Furthermore, models of 3 evaluation indices were established, which are number of people passing per unit time, average delay per people, and average yield number per people at intersection. Moreover, the entropy weight method was taken to decide the weight of these 3 indices, and to calculate the comprehensive efficiency evaluation of intersection operation, with the standardized matrix. Finally, case study work was carried out to evaluate the comprehensive efficiency of 4 types of intersections in arterial considering pedestrians yield rule, which is the intersection between arterial and arterial (IAA), intersection between arterial and subarterial (IAS), intersection with one-time (IAA-1, IAS-1), and two-time (IAA-2, IAS-2) crossing of pedestrians. The relevant results show that the impact of an increase in the number of pedestrians on the combined efficiency of arterial intersections can vary dramatically in different scenarios. Therefore, in the implementation process of “yielding to pedestrians,” the flow fluctuation characteristics and channelization of each intersection should be taken into account, and the corresponding phase changes should be based on pedestrian and vehicle volumes to improve the efficiency of all parts, such as pedestrians and drivers.

Signal timing and geometric design at contraflow left-turn lane intersections

As a type of innovative intersection, Contraflow left-turn lane (CLL) intersection, has been increasingly implemented at the signalized intersections for increasing the leftturn capacity. This study developed a systematic method for determining the length of CLL and the signal timing plan for implementing CLL at signalized intersections. At first, the signal timing and geometric design requirements in the implementation of CLL were analyzed. Then, these requirements were thoroughly considered in the development of the proposed method. A case study was conducted for evaluating the operational benefits of redesigning a real-world CLL intersection using the recommended signal timing plan and CLL lengths. The results of the case study show that the redesigned CLL intersection outperforms the existing CLL intersection in terms of the average traffic delay, average vehicle travel time, and average queue length, and the CLL intersection can achieve its best performance at the recommended CLL lengths. These results indicate that the proposed method can provide useful guidelines to traffic engineers in the implementation of this innovative intersection design.

Identification of Road Network Intersection Types from Vehicle Telemetry Data Using a Convolutional Neural Network

GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver’s behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips.

Multi types and reasonable space

Accattoli, Dal Lago, and Vanoni have recently proved that the space used by the Space KAM, a variant of the Krivine abstract machine, is a reasonable space cost model for the λ-calculus accounting for logarithmic space, solving a longstanding open problem. In this paper, we provide a new system of multi types (a variant of intersection types) and extract from multi type derivations the space used by the Space KAM, capturing into a type system the space complexity of the abstract machine. Additionally, we show how to capture also the time of the Space KAM, which is a reasonable time cost model, via minor changes to the type system.

Capacity Estimation at All-Way Stop-Controlled Intersections Considering Pedestrian Crossing Effects

All-way stop-controlled (AWSC) intersections are a typical type of unsignalized intersection that has been commonly used when both intersecting roads have a relatively low vehicular traffic demand. At an AWSC intersection, vehicles proceed through the intersection under a “first come, first served” rule. Since AWSC intersections are often deployed in residential or business areas where pedestrian crossings tend to be inevitable. When a driver who has the right-of-way yields to the crossing pedestrians, the right-of-way at the intersection might be reallocated, which could result in capacity losses to the current approach. On the other hand, the conflicting vehicular traffic movement(s) to the current approach may use this gap and proceed through the intersection ahead of this vehicle. Nevertheless, the effects of pedestrian crossings at AWSCs have not been considered by the existing capacity modeling methods. This research develops a method to evaluate the capacity changes caused by pedestrian crossings at AWSC intersections. Field data collected at two representative sites were collected for model calibration and validation. A sensitivity analysis was conducted to reveal the combined effects of different pedestrian crossing movements and under various pedestrian crossing demands. Results indicated that pedestrian crossing movements could change the capacity at AWSC intersections, but the effects vary according to pedestrian activities and vehicle directions.

A Study of Driver Eye-Glancing Behavior at Two Different Types of Unsignalized Intersections Based on SHRP2 NDS Data

The study aimed to analyze driver visual workload at unsignalized intersections with wide medians on high-speed divided highways. The study focused on two types of traffic movements: direct left-turns from minor roads at conventional intersections and right-turns followed by U-turn movements at restricted crossing U-turn (RCUT) intersections. A total of 430 left-turn trips and 40 right-turn followed by U-turn trips were collected from the Second Strategic Highway Research Program Naturalistic Driving Study database. Researchers analyzed the data from driver eye-glancing videos, demographic information, and driving history. The entropy rate of each trip was calculated as an indicator of the driver's visual workload and was treated as the dependent variable for the statistical analysis. Entropy rate is the metric of the randomness associated with drivers’ scanning patterns. The higher the entropy rate, the higher the workload. A comparative study of these two movements at these two types of intersections was conducted. Statistical analyses indicated that drivers at RCUT intersections engaged in less random scanning and longer average fixation and spent more than 70% of the time looking forward during the whole movement. Younger drivers at both types of intersections had higher entropy rates. Additionally, drivers at conventional intersections with higher annual average daily traffic (≥30,000) had higher entropy rates. These results could provide a better understanding of driver workload and its safety implications at different types of unsignalized intersections.

Pedestrian Crossing Decisions During Pedestrian Transition Signals at Signalized Intersections

In China, pedestrian transition signals that are set to green flashing or countdown signals are activated between red and green signals. They are used to remind pedestrians that the subsequent vehicle phase is ready to start and that pedestrians cannot start to cross; otherwise, pedestrian–vehicle conflicts may occur. However, during this phase, pedestrian crossing decisions are complex and are affected by many factors, such as distractions and companions. This paper aims to predict pedestrian crossing decisions by extracting these factors under various conditions. Four types of intersections in Changsha, China, including different transition signals (flashing green/countdown) and different crossing facilities (with a refuge island/with a median strip/without crossing facilities), were selected. A total of 1021 samples were collected. The assessment of crossing risks revealed that although crossing facilities can reduce risks, crossing pedestrians are still at a high risk. The results of logistic regression models indicate that the factors affecting crossing decisions vary at different intersections, and no single factor exerts the same impact.

GIS Analysis on Road Accident and Road Safety: A Case Study of Road Geometry in Kajang, Selangor

Malaysia has recorded the 8th highest number of fatal road accident. This is due to a few factors such as inexperience, incapacity and risk behaviours of driving. Other than that, road conditions also play a role. Road Accidents need to be seriously taken into action by the government and agencies involving in road safety. Geographical Information System (GIS) technology had been used in this study to store, retrieve and view data for number of accidents that occurred and to compare with road geometry characteristics. This study focused on the relationship between road geometry and road accident cases in chosen study area which is Kajang, Selangor. GIS offers various advantages thanks to its integrated statistical analytical capabilities. GIS technology has widely been used to process the map of accident-prone locations. Moran’s I technique is a tool used that measures the overall spatial autocorrelation for this study. The relationship between road geometries and road accidents was determined with the use of Getis Ord Gi* tools that is provided in GIS technology. By identifying the hotspot location, spatial analysis is very helpful and can provide a technique to increase the safety of these accident locations. At the end of this study, map of hotspot accident location and the connection between road geometries had been successfully analysed where it was found that accidents occur most frequently in T-intersection where is was recorded the highest number of accident cases for the intersection type. &nbsp

Interdependence of driver and pedestrian behavior in naturalistic roadway negotiations

Objective This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction. Methods A 228 epoch vehicle-pedestrian interaction dataset was extracted from a large naturalistic driving data collection effort, which included vehicle, pedestrian, and contextual information (e.g., intersection type, jaywalking, vehicle maneuver, and lead vehicle presence). An expanded Actor-Partner Interdependence Model (APIM) was used to analyze driver-pedestrian dyads using driver and pedestrian standard deviations of velocity as the independent variables and wait times as dependent variables. APIM structural equation models were augmented to include driver effects (i.e., lead vehicle and maneuver type) and pedestrian effects (i.e., lead pedestrian, crossing group size, crossing direction). Results The level of protection afforded by an intersection had an effect on the extent of driver-pedestrian dyadic behavior. Interactions in undesignated crossings (i.e., jaywalking) were associated with interdependent behavior whereas interactions in designated crossings (i.e., crosswalks and parking lots) showed a partner effect on the driver’s wait time but no significant corresponding partner effect on the pedestrian. Finally, protected intersection interactions (i.e., traffic lights and stop signs) demonstrated no significant partner effects. Conclusions The difference in behavior patterns associated with the intersection type and level of protection shows that context can mediate the level of negotiation required between drivers and pedestrians. These findings inform how context and driver-pedestrian interactions should be incorporated in future modeling efforts which may, ultimately, support design of automated systems that are able to interact more safely, efficiently, and socially.

Semantics for Combinatory Logic With Intersection Types

There is a plethora of semantics of computational models, nevertheless, the semantics of combinatory logic are among the less investigated ones. In this paper, we propose semantics for the computational system of combinatory logic with intersection types. We define extensional applicative structures endowed with special elements corresponding to primitive combinators. We prove two soundness and completeness results. First, the equational theory of untyped combinatory logic is proven to be sound and complete with respect to the proposed semantics. Second, the system of the combinatory logic with intersection types is proven to be sound and complete with respect to the proposed semantics. The usual approach to the semantics for calculi with types that can be found in the literature is based on models for the untyped calculus endowed with a valuation of type variables which enables the interpretation of types to be defined inductively. We propose, however, a different approach. In the semantics we propose, the interpretation of types is represented as a family of subsets that satisfies certain properties, whereas for a given valuation of term variables, the interpretation of terms is defined inductively. Due to the wide applicability of semantics of computational models, the presented approach could be further developed to other computational models and beyond—to current and foreseen application of semantics to large distributed systems and new challenging technologies.

ROUNDABOUTS AS AESTHETIC ROAD SOLUTIONS FOR ORGANIZING LANDSCAPES

In the transportation systems are different types of intersections. Among them, roundabouts are considered the safest solution. The application of roundabouts is conditioned by many factors. One of them borders on area development. Roundabouts, among others, are localized in important city places, thus serve as a landmark in the city or are localized on the boundaries of areas with different communication functions, thus constituting an organizational landscape feature. Sometimes, they diversify the space of a street or close the perspective of a monotonous street. Hence, beyond the basic functions, roundabouts with the appropriate central island arrangements can also play the function of organizing the architecture of the local area. The ways and possibilities of roundabout central island arrangements are presented in this article. An interesting central island arrangement shows that roundabouts play an esthetic and organizing landscaping role.

Selection of Intersection Control Type in Urban Areas

Abstract In Egypt, urban planning projects usually do not consider the intersection type. This study developed charts for practitioners that could be used to determine the optimal intersection. The paper studied intersections including; signalized, roundabout, and two-way stop control. It provides a study for the intersection at the targeted level of service D. The traffic patterns were used to calculate the intersection’s capacity and the average control delay. The combinations of turning distribution, percent of heavy vehicles, and type of control were considered. The relationship among capacity, delay and traffic volume has been used to deduce the minimum required lane configuration for each intersection. The results indicated that the roundabout is the best alternative for intersection control with maximum capacity and minimum delays for medium traffic. However, signalized intersection is the best alternative at heavy traffic volumes and a high proportion of left-turning volume.

Disaggregating How Asian Americans Define a Healthy Diet: Exploring Generational and Intra-family Differences Through Dyadic Interviews

ObjectivesAsian Americans (AAs) experience a complex and growing non-communicable disease burden linked with dietary behaviors. Dietary norms, which significantly contribute to eating behaviors, face a complex set of Western and Asian socio-ecological influences in AA communities. However, little is known of how dietary norms (such as definitions of a “healthy diet”) are transmitted, intersect, and differ in a multigenerational family environment. MethodsAA young adults and family members were recruited to participate in dyadic, 60-minute Zoom interviews (Jun - Dec 2021). Young adults and family members were interviewed separately on their respective eating behaviors, including perceived definition of a healthy diet. Recordings were inductively analyzed to identify themes related to different facets of a conceptualized healthy diet. ResultsA total of 41 AAs were interviewed (76% female, 61% US-born, age 19–69), including 20 young adults and 21 family members (10 parents, 9 siblings, 2 cousins). Diet healthfulness was defined in three dimensions: types of food, cooking methods, and intersection of diet with holistic wellbeing. Young adults consistently emphasized the role of their parents in shaping their idea of a healthy diet, particularly Asian-specific conceptualizations of balance (e.g., “yeet hay”). However, while these ideas were particularly influential among older participants, younger participants also emphasized the intersection of diet with emotional and spiritual well-being. Both stressed cooking methods as indicators of healthfulness. While a “healthy diet” was conceptualized through intake of food groups such as fruits, vegetables, and proteins, foreign-born and older participants put a larger emphasis on dietary restrictions (e.g., completely cutting out food groups, such as fast food), while US-born and younger participants stressed enjoying foods in moderation. ConclusionsThe differential understanding and impact of AA norms related to diet healthfulness highlights the need for a disaggregated understanding of dietary attitudes and behaviors in health-based interventions for AAs of different generations, particularly family-based programs. Findings call for future mixed-methods research to explore the complex dietary norms identified. Funding SourcesSouth Asian Health and Research Group (SAHARA).