Mid-block Sections Research Articles

Analysis of Vehicular Travel Time along the Urban Corridor Using Bluetooth Based Data

There has been a tremendous increase in vehicular growth in the last few decades, which has increased congestion in urban areas. Travel time is one of the measures to understand the congestion levels of the urban corridors. The increased use of Bluetooth devices by vehicular drivers may enable the dynamic data collection of travel time through Bluetooth sensors in urban areas. In this context, the objective of the present study is to evaluate the travel time of vehicles using Bluetooth devices and check the reliability of this Bluetooth technology for traffic studies. To fulfil the stated objective, a Bluetooth application was installed on a smartphone in order to collect the vehicular travel time on a selected corridor. A video camera was setup for traffic data collection at a mid-block section, which is used for the validation of the collected travel time data. The K nearest neighbor (KNN) model was developed with travel time (TT) data. The average TT of cars and other vehicles is estimated and found to be higher in the evening compared to the morning. From the developed KNN model, it is also found that the predicted TT for the next time intervals shows that they follow the trend of the actual travel time data obtained from the Bluetooth app. From the results of this study, it is feasible to estimate the travel time in urban areas using Bluetooth devices, which is useful for engineers and planners for suitable policy decision.

Influence of pedestrian movement on speed of vehicles, capacity and level of service of urban midblock sections

Side friction factors are all those actions related to the activities taking place by the sides of the road and sometimes within the road, which interfere with the traffic flow on the travelled way. They include but not limited to pedestrians, bicycles, non-motorized vehicles, parked and stopping vehicles. These factors are normally very frequent in densely populated areas in developing countries, while they are random and sparse in developed countries making it of less interest for research and consequently there is comparatively little literature about them. The objective of this thesis is to analyze the effect of these factors on capacity of urban roads. The present study is mainly focused on individual effect of the friction factors like pedestrians crossing the road on capacity of urban roads. Data on two sections of 4-lane divided road were collected in Hyderabad with pedestrian cross flow. These are taken as the base sections. Three basic parameters of traffic speed, volume and density are used for estimation of traffic carrying capacity of a road. For determination of speed-volume relationship in heterogeneous traffic condition, the volume calculated by total vehicles recorded for each counting period were converted into equivalent number of PCUs. Mean stream speed or weighted space mean speed is calculated and used in the present study. The present study demonstrates that mix traffic stream can be converted in to an homogeneous equivalent of passenger cars by multiplying the total traffic volume (in veh/hr) by a stream equivalency factor (K). It will avoid the problem of estimation of PCU values for individual vehicle categories in the traffic stream. The stream equivalency factor (K) will depend on traffic composition and a regression equation is developed in this study to estimate K-value on urban roads.

Modeling of Risk Factors for Vulnerable Road Users’ Crashes on National Highways Passing through Medium-sized Indian Cities

The safety of vulnerable road users, such as pedestrians, bicyclists and motorised twowheelers, on national highways passing through urban areas has been a significant concern in developing countries like India. This study analyzes Visakhapatnam police crash data (2014-2017) to identify high-risk road segments to vulnerable road users. Establishing negative binomial models helps analyze risk variables for fatal and injury crashes concerning several susceptible road users on mid-block sections. Further, this study focuses on site-specific information collected from road-safety audits, speed, average daily traffic, length of road section and percentage of two-wheelers, also considered to fully help understand the likelihood of a crash. The statistical analysis of this paper identified the risk variables, like length of road segment, presence of service roads, land-use type (i.e., commercial/mixed land use), number of curves and average daily traffic associated with the incidence of fatal and injury crashes of vulnerable road users in Visakhapatnam city, India. The conclusions highlight the crucial safety measures that transportation planners and policymakers must take to create a more secure environment for road users. Keywords: Vulnerable road users, Mid-block sections, Negative binomial models, Crash, Count-data models, Safety measures.

Signalized intersections and mid-blocks autonomous wireless systems based on level of service (LoS) determination IoT

The study tries to evaluate the effectiveness of different mid-blocks and signalized intersections using Level of Service (LOS) and v/c ratio. The present study attempts to determine the Level of Service (LOS) and v/c ratio at different mid-blocks and signalized intersections. The analysis of the field data collected is done using the empirical formulae provided in INDO HCM 2017 for determination of LOS for mid-blocks and signalized intersections.The study also focuses on computing V/c ratio for mid-block sections chosen in the study to understand the intensity of congestion on the roads. If the v/c ratio is greater than 1, it represents that the demand of the traffic on that road exceeds its capacity.The present study acts as an empirical guide to understand the effectiveness of important mid-blocks and signalized intersections in Hyderabad city as autonomous wireless systems.

Development of Capacity Estimation Models for Multi-lane Roads under Heterogeneous Traffic Conditions

The objective of this study is to develop capacity models to estimate the traffic carrying capacity of multi-lane roads with heterogeneous traffic flows. Since capacity is an important parameter in traffic engineering studies, it is vital that it is accurately estimated. Most of the current capacity estimation methodologies available are developed based on homogeneous traffic flows and hence not conducive for use in heterogeneous traffic conditions observed in Sri Lanka. Therefore, this study is done to fulfill the requirement of a capacity estimation model for this purpose. Fifty midblock sections were surveyed using manual flow data collection methods and a novel Google Application Program Interface (API) based method for speed data collection. Based on the collected data, capacity values were developed following the fundamentals of traffic flow. Regression models were built to estimate four-lane and six-lane capacity values based on roadway characteristics such as effective lane width, access point density, median type, and built environment condition. Separate models were developed because the impact these characteristics have on four-lane and six-lane roads are different. The models showed good fit with R2 values of 0.81 and 0.86 for four-lane and six-lane roads, respectively. The base capacity value of a four-lane urban road was estimated to be 2044 pcu/h/l and for a six-lane sub-urban road section 2108 pcu/h/l. The outcomes of this study can be used to develop capacity guidelines in countries with heterogeneous traffic conditions for capacity estimation.

Development of Composite Pedestrian Level of Service Model in Urban Milieu at Midblock Sections

The pedestrian mode is gaining recognition as a basic building block in urban system design. Increasing attention is being given to developing vehicle-free zones to reduce urban pollution and return the inner city to its former role as a place for personal interaction. Attempts are being made to improve the walking experience, to make it more safe, convenient, and attractive especially in urban milieu to promote sustainable development. To provide the best pedestrian environment, it is necessary to understand pedestrian level of service. Conventional techniques of LOS determination consider the basic mobility characteristics of the pedestrian. The objective of this study is to develop a more accurate method of estimating level of service of pedestrians by developing composite indicators that integrate both qualitative and quantitative factors affecting both pedestrian and roadway conditions in the pedestrian movement. The relative importance of these multifaceted factors is assessed using Saaty scale and an Analytic Hierarchy Process is applied to determine the level of service. Field investigations include road network characterization and inventory studies, vehicular / pedestrian traffic characterization studies, pedestrian opinion surveys, expert opinion surveys. The approach has been applied to a major arterial road in Hyderabad city, Telangana, India in Mehdipatnam zone where pedestrian interactions are relatively high due to the commercial activity along the corridors. The stretches considered in the study exhibited Level of service "B" and "C" which implied necessary improvements to promote a good level of service.

Quantification of Congestion and Level of Service Determination for Urban Road Infrastructure

Level of Service is an important measure for describing traffic condition in terms of travel time, speed, convenience, comfort, and safety. The aim of present study is to measure the congestion in mid-block sections using the corresponding average speed values and determine the LOS of the mid-block sections of urban roads based on v/c values. Three mid-block sections were chosen and video-recording method was used for collecting data. Based on the average speed values the level of congestion was determined and the level of service of each mid-block section was also determined by using the v/c ratios. No/low level of congestion was observed in Section- I and Section-III whereas moderate congestion was observed in Section-II. As a result of congestion, fuel consumption will be more, which in turn causes air pollution. When the dynamic PCU values were used for v/c determination, the LOS was A in all the sections.

Heterogeneous road traffic noise modeling at mid-block sections of mid-sized city in India.

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 (LAeq, 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, forLeq, and a value of 0.86, 0.80, and 0.84 for L10. The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named "Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)" was developed for the benefit of field engineers and policy planners.

Modelling crash severity outcomes for low speed urban roads using back propagation – Artificial neural network (BP – ANN) – A case study in Indian context

This work analyses influence of road, weather and crash-specific factors on crash severity outcomes for low-speed urban midblock sections and intersections, for day and night time, using Backpropagation–Artificial Neural Network (BP–ANN). Five-year crash data (2015–2019) from 82Km urban road network of Patna, India was used for the study. The road factors include pavement width, distress condition, marking; shoulder type, condition; road section type as mid-block, intersection and intersection control. Weather factors include season of crash, fog or rain at crash time. Crash factor include collision partner, type and crash time. The most appropriate BP–ANN model architecture was estimated using Misclassification-Rate. It was observed that midblock segments witness higher severities during daytime, whereas intersections witness higher severities during night. Controlled intersections are safer compared to un-controlled intersections. Pavement distress greatly increase the chance of higher severities. Narrow roads record greater severities during day due to lack of surveillance.

Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications

The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed “time spent in conflict (TSC)” is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs. The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs. The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.

Improving cycling safety through infrastructure design: A bicycle simulator study

This study investigated the impact of two road treatments on cyclist behaviour using an bicycle simulator. The treatments were sharrows (presence or absence) at a roundabout and widening an on-road bike lane (width 1.2 m or 1.8 m) on a curved mid-block section of the road. Outcomes collected included count (e.g. number of lane excursions) and continuous responses (lane position upon entry into the roundabout, in metres from the curb). A convenience sample of 21 Perth-based participants was recruited between 1 July and 30 September 2021. The results found that the presence of sharrows was not significantly associated with the distance from the left curb when entering a single lane roundabout. However, females rode 0.44 m further from the left curb when entering the roundabout than males, which was significant (95 % CI = 0.02–0.85; p = 0.04). Riders, aged 26–40 years, also rode 0.61 m further from the left curb when entering the roundabout than riders aged 18–25 years, which was also significant (95 % CI = 0.18–1.05; p = 0.01). The rate of bike lane excursions significantly decreased by 68 % for the wide bike lane on the curved mid-block section of the road compared to the standard width bike lane (IRR = 0.32; 95 % CI = 0.11–0.88; p = 0.03). Riders, aged 26–40 years, had a significant increased rate of 8.72 excursions than riders aged 18–25 years (IRR = 8.72; 95 % CI = 3.32–22.89; p < 0.001). This study highlights that increasing the bike lane width on curved sections of mid-block roads may benefit cyclists. However, the value of sharrows in a single lane roundabout is less clear.

Comparison of Pedestrian Delay Models at Signalized Midblock Crossings Under Mixed Traffic Conditions

In the field of transportation engineering, one of the most important factors for the traffic engineers and road users (pedestrian and vehicle) is the delay which governs the LOS of a facility. Past studies shows that various delay models like HCM, Webster, and Austroads work well for the homogeneous traffic conditions. However, Limited studies were done on mixed traffic conditions like in India and in other developing countries. Apart from that, all the models were developed based on the conditions of signalized intersection whereas signalized midblock crosswalks were completely overlooked by the researchers. The present study aims to compare pedestrian field delay at signalized midblock sections with the existing models. Six mid-block crossings have been selected from Hyderabad, India and data was collected using video-graphic technique. Field Pedestrian waiting delays were measured by plotting queue length against cycle time and area under the curve was calculated using Simpson's 1/3rd rule. Field crossing delay was calculated as the difference between actual crossing time and ideal crossing time. The performance of all the models were evaluated using t-test and other performance indices like MAPE, RMSE and R2. It was observed from the statistical tests that there is a significant difference between the means of field delay and delay by existing models. Also all the existing models yielded an error of more than 20% with an R2 value of less than 0.3. Calibrated results can be used in the modelling of new pedestrian delay for signalized midblock crosswalks.

Estimation of vehicular delay in presence of illegally crossing pedestrians and determination of LOS using cluster analysis at midblock sections of urban roads

Estimation of vehicular delay in presence of illegally crossing pedestrians and determination of LOS using cluster analysis at midblock sections of urban roads

Pedestrian safety analysis at urban midblock section under mixed traffic conditions using time to collision as surrogate safety measure

Pedestrian safety analysis at urban midblock section under mixed traffic conditions using time to collision as surrogate safety measure

Effects of Pedestrians’ Assertiveness on Drivers’ Yielding Behavior at Mid-Block Sections: An Application of Bayesian Structural Equation Modeling

Crossing roads at mid-block sections often creates ambiguity about priority between pedestrians and drivers, resulting in conflicts, road crashes, death, and human injury. To share the road space safely, they need to anticipate other user behaviors whilst maintaining and modifying their own habitual and desired maneuvers. This study investigates the effect of pedestrian assertive behaviors and vehicle user characteristics on driver yielding at mid-block sections. Road users’ interactions were observed in a dense mixed land use urban area of Central Auckland, New Zealand. Bayesian structural equation modeling is used to find interrelationships of multivariate data. The result shows that yielding levels decrease when the vehicle speed increases and they are not part of a platoon. Pedestrians’ direct signals (i.e., hand gestures) can increase drivers’ willingness to yield. Conversely, pedestrians, who tend to run or cross heedlessly through the traffic, are less likely to modify driver behavior in a high-speed environment. However, these factors are mediated through vehicle speed-related factors. Women are more likely to be given priority compared to men, especially when they have slow crossing speed. The study offers a better understanding of road users’ interactions outside controlled crossings. It provides evidence why it is important to reduce operating speeds in areas where there is a high demand for sharing between vehicles and vulnerable road users and mid-block crossings. Road users can be better informed to understand more gesture communication combined with appropriate engineering practice, such as traffic calming, and where appropriate re-prioritization of road space to influence drivers’ operating speeds.

Predicting Pedestrian Crashes in Texas’ Intersections and Midblock Segments

This study analyzes pedestrian crash counts at more than one million intersections and midblock segments using Texas police reports over ten years. Developing large-scale micro-level analyses is challenging due to the lack of geographic information and characterization at a statewide scale. Therefore, key contributions include methods for obtaining many points and related variables across a vast network while controlling for traffic control variables (signalized intersections), highway design details, traffic attributes, and land use information across multiple sources. The analytical framework includes a method to estimate the intersection and midblock segments’ geometry and characteristics, data processing of historical pedestrian crashes and mapping to the estimated geometry, and the development of predictive models. A negative binomial model for crash counts across the state of Texas and within the city of Austin suggests that signalized intersections, arterial roads, more lanes, narrower or non-existent medians, and wider lanes coincide with higher crash rates per vehicle-mile traveled (VMT) and per walk-mile traveled. The analysis suggests that daily VMT increases the likelihood of pedestrian crashes, and midblock segments are more vulnerable than intersections, where one standard deviation increase in VMT caused an increase in crashes at intersections and midblock sections of 52% and 187%, respectively. Furthermore, the number of intersection crashes in Austin is higher than in the rest of Texas, but the number of midblock crashes is lower. Analysis of the Austin area suggests that the central business district location is critical, with midblock crashes being more sensitive (240%) in this area than intersection (78%) crashes. Moreover, a significant inequity was found in the area: an increase of USD 41,000 in average household income leads to a reduction of 32% (intersections) and 39% (midblock) in pedestrian crash rates.

Level of safety and crossing warrants for cross-walks using surrogate safety measures

Pedestrians are the most vulnerable road users and they are highly susceptible to conflicts at mid-block cross-walk locations. They are prone to a high risk of injury from vehicular impact incidents and their decision making behaviour at unmarked cross-walks (UCWs) is more critical than at marked cross-walks (MCWs). The objective of this study was to determine level of safety thresholds and warrants for both marked and unmarked pedestrian crossing facilities at urban mid-block sections under mixed traffic conditions. Data were collected at MCWs and UCWs in four different locations in Trivandrum city, India. The crossing behaviours of pedestrians at both types of cross-walks were compared by analysing characteristics such as gap acceptance, a surrogate safety measure (post-encroachment time (PET)), waiting time, crossing time, platoon size, type of critical vehicle and critical gap. The multiple linear regression technique was used to develop gap acceptance models for MCWs and UCWs. The pedestrian level of safety (PLS) criterion is proposed; the PLS describes the safety of pedestrians at cross-walks and warrants for crossings based on PETs. The developed PLS thresholds and the pedestrian–vehicle interaction values were correlated to the PETs to develop the pedestrian crossing warrants.

Dynamical systems approach for travel time prediction in intermediate section under mixed traffic conditions

An urban arterial can be considered a series of intersections, intermediate sections (defined as those that link intersections and mid-block areas), and mid-block sections. The intermediate section is characterized by varying speed characteristics at its entry and exit. A vehicle exiting the intersection and entering the intermediate section would accelerate and gradually attain uniform speed when it leaves the intermediate section (entry to the mid-block). Most of the reported travel time studies considered the delays at intersections and the travel times in mid-blocks to get the network level travel time without paying attention to the variability of speeds and travel time at intermediate sections. The present study concentrates on the travel time estimation of the intermediate section, which is not well-discussed in the literature. A speed estimation scheme was developed for the entry and exit of the intermediate section (characterized by varying speed ranges) using a dynamic model-based estimation scheme that captured the speed variations effectively. Intermediate link travel time was then estimated using a weighted average speed-based method. The effects of heterogeneity and limited lane discipline in the traffic stream of mixed traffic were considered using two model formulations, one that represented the traffic stream in Passenger Car Units (PCU) and the other in different vehicle classes for travel time estimation. The developed estimation schemes can be used as a possible application in Intelligent Transportation Systems (ITS) for real-time estimation of travel time at urban arterials under mixed traffic conditions.

Anticipated Collision Time (ACT): A two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment

Surrogate Safety Measures (SSMs) are widely used to assess potential crash risk proactively. Notably, most of the existing safety indicators are fundamentally designed to capture the rear-end collision risk. However, in reality, the traffic dynamics involve the simultaneous interaction of multiple vehicles on a 2-dimensional (2D) surface, which results in a broad spectrum of collision patterns, such as head-on, side-swipe, rear-end, and angled collisions. This study proposes a novel 2D surrogate safety indicator called Anticipated Collision Time (ACT) to capture the risk pattern corresponding to all collision types using a single measure. We also devised a procedure to automatically detect conflict situations and extract ACT profiles as well as crash-type information from the trajectory data. The primary inputs to the ACT estimation are the shortest distance between the vehicles and the closing-in rate. This study also introduces another safety indicator derived from the ACT profile called Time of Evasive Action (TEA). TEA primarily captures the time at which a vehicle commences to respond, in terms of deceleration, when it encounters an unsafe situation. Such a measure helps to understand the response pattern of different vehicles/drivers to a potential collision. We also derived Time Exposed ACT (TE-ACT) and Time Integrated ACT (TI-ACT) from the ACT profile to capture crash exposure and severity. To highlight the potential of ACT, the Powered Two Wheeler (PTW) safety in an urban environment was analyzed with trajectory data collected from a busy urban midblock section. The results emphasize the capabilities of ACT and the other derived indicators to capture crash risk proactively. It is incontrovertible from the analyses that the ACT is opening up a new avenue for a comprehensive investigation of the safety of various transport facilities, irrespective of geometry and traffic scenario.

Capacity Analysis of Urban Arterial Midblock Sections Under Mixed Traffic Conditions

Capacity Analysis of Urban Arterial Midblock Sections Under Mixed Traffic Conditions