Vehicular Speed Research Articles

Vehicular Communication using Balanced Centralized and Decentralized Cluster Heads

A new approach to vehicular communication employing equal weights for distance and vehicular speed for centralized and decentralized communication is presented. The main objective of this work, which is to establish utilization expression and characteristics for an optimized balanced vehicular communication is achieved. The technique is based on analyzing effect of communication process (centralized, decentralized) on transmission efficiency and probability of failure. The analysis using utilization function, cluster head selection time, and end to end transmission time. The simulation and analysis concluded that the decentralization approach is more efficient compared to the centralized approach, so combination of both is proved to be effective. The work also uncovered the need for optimization of vehicular speed relative to transmission radius and use of zoning to effectively improve transmission efficiency. Mathematical models are presented that covers a critical relationship between probability of transmission failure, cluster head selection time and end to end delay. Also, an important mathematical expression that considers cluster head selection time and end to end delay and their effect on connection utilization is presented. The work proves that combined centralized and decentralized techniques using balanced weights approach is effective using dynamic weights selection algorithm that determines optimum weights for both used variables (distance, Vehicular speed).

Effects of urban road environment on vehicular speed. Evidence from Brescia (Italy)

Inappropriate speeds are key factors that may affect the occurrence and the severity of road crashes. Although rural roads are influenced by more severe crashes than urban roads, perhaps due to the higher vehicular speeds, the latter suffer from a higher frequency of crashes. Therefore, exploring factors affecting the vehicular speed in the urban area is crucial. The literature provided several models to usually estimate the operating speed (i.e., V85) in rural roads. However, further investigations are needed to provide these estimations in the urban areas. In addition, these models often estimate the 85th percentile of the speed distribution, that cannot represent the entire distribution. Therefore, the problem of the speed prediction distribution is also a challenge in urban roads. This paper addresses this challenge by exploring the effects of some road factors on the vehicular speed along segments of urban roads. First, this speed is modelled as a random variable with a normal distribution. Next, by using 11,466 car spot speed data collected along a portion of the urban road network of city of Brescia (Italy), two multiple linear regression models were run for the estimation of the speed mean and the related standard deviation, respectively. Preliminary results showed that the presence of median, the bus stop density, the presence of curb and the type of adjacent land are significant predictors of the vehicular speed distribution on urban roads. These results may support road management agencies to set proper actions on speed management, especially for existing roads and/or critical section roads in urban areas.

Experimental evaluation of gear-shift and internal-combustion engine variables on fuel consumption, noise and pollutant emissions

Although variability of noise and pollutant emissions are usually associated with vehicular speed and acceleration, driving style, mainly related to gear-shift and internal engine variables such as revolutions per minute (RPM) or engine load (EL), can also play a key role. Moreover, the contribution of each variable for fuel consumption, noise and pollutant emissions can vary for different vehicle-motorization types. However, the effect of such internal variables on noise and pollutant emissions is not fully exploited in the literature. Thus, this work aims to assess the impact of the gear selection, RPM, and EL on fuel consumption, and carbon dioxide (CO2), nitrogen oxides (NOx), and noise in terms of sound power level (Lw) emissions for a diesel passenger vehicle. This is focused on a speed and gear-based controlled on-road environment. Internal observable (fuel consumption, RPM, and EL) and kinematic (speed and acceleration) variables were recorded on a second-by-second time basis using an On-Board Diagnostic System, and noise data were recorded with a Sound Level Meter. Pollutant emissions were estimated using the Vehicle Specific Power (VSP) methodology with a 1Hz frequency. In this study, Clustering and Disjoint Principal Component Analysis is applied to find patterns hidden in data. An Ordered Logit model to predict the gear based on exploring kinetic and internal engine variables, that are influenced by driver’s driving style, is developed. Preliminary results highlight the potential of the developed model and show the potential influence of gear selection for minimizing fuel consumption, noise and pollutant emissions. These findings establish a foundation for developing a sustainability gear-shift indicator not only focused on minimizing fuel consumption, but also noise and pollutant emissions. These are relevant to understand vehicle performance and alleviate the relative impacts of the driving style if integrated into vehicle engine control units.

Analysis of Vibro-Acoustic Path for Quantification of Tire-Pavement Interaction Noise Using a Two-Wheeler

Abstract The objective of this research was to explore methods of signal analysis to study characteristics of the path for tire-pavement systems. The scope encompassed utilization of the state-of-the-art signal processing techniques to analyze the vibro-acoustic path for quantification of tire-pavement interaction noise of different types of pavement systems. Data collection included actual field noise and acceleration measurements on asphalt concrete (AC) and cement concrete (CC) pavement sections at varying speeds. First, single frequency filtering (SFF) method was utilized to minimize the tread impact effect and highlight the path resonances based on which the absorption metrics were proposed. Sharp peaks in the average SFF spectra indicated lower absorption, and smooth peaks reflected higher absorption capacity of a pavement system. Second, the input-output relation of a dynamical system was utilized to compute the transfer function, which helped characterize nonlinear properties of the tire-pavement system. For a given pavement type, based on the inconsistency found with frequencies across all speeds, the path behavior was inferred as nonlinear. The frequencies corresponding to CC pavement were higher by about 7% compared to AC. Furthermore, at each speed, root mean square of CC pavement was about 11–18 dB higher than AC. The framework proposed in this research will help quantify the contribution of pavement types to the overall vibro-acoustic path while also being able to measure the variation in roadway’s contribution with vehicular speed.

Effects of Berlin speed cushions in urban restricted speed zones: a case study in Bari, Italy

The widespread European policy towards urban sustainable mobility requires some engineering-related interventions on the existing urban road network, such as traffic calming measures. There is a substantial amount of research assessing the effects of different traffic calming measures, even if there is no unanimous evidence for some of them, such as speed cushions (in particular Berlin speed cushions). Some research on speed cushions has been conducted, even if different results were achieved, also varying with the country and context of installation. Moreover, some of these studies are old and they need to be updated, given the continuous transformation of urban environments. In agreement with the City of Bari, thanks to ASSET-Puglia Region funds, the Italian Ministry of Infrastructures and Transport has granted permission to install speed cushions on three urban streets located in restricted speed zones (≤30 km/h) in the City of Bari, to test their effects on vehicular traffic. In this context, this article is aimed at assessing the preliminary results obtained, considering the effects on vehicular speeds. The study design is a typical before-and-after study, in which speeds are measured using a laser speed gun on the three selected road segments before and after the implementation of speed cushions (for each segment, the cushion has a different width). Visual observations were also useful to detect the effects of speed cushions on trajectories and speeds of two-wheeled vehicles. The preliminary analysis of speed profiles revealed a consistent decrease in speed for all the three test sites (in particular the operating speed V85, which is reduced by up to approximately 30%). Moreover, it seems that the decrease in speed is more evident as the cushion width decreases.

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

Decentralized and centralized vehicular communication is investigated in this work using Gaussian interpolation function with cluster head (CH) selection technique. The work uncovered that the best communication approach is to use both centralized and decentralized vehicular communication as combining them will achieve a much more uniform results as a function of communication radius values and vehicular speed. It is also found that vehicular speed contributes negatively to the efficiency of data communication if the relative speed of the vehicles to the communication radius is limited by their ratios. Mathematical expression is presented that relates probability of successful transmission to communication radius for both centralized and decentralized techniques with data proving the importance of the spread parameter within the Gaussian interpolation in a tabulated form, and explained to prove the adaptability of the function used. It is also shown in this work that weights affecting CH selection, thus using Gaussian interpolation is proved to be important as a weighting function in an a adaptive and dynamic vehicular ad-hoc networks (VANETS) covering both vehicle to vehicle (V2V), and vehicle to infrastructure (V2I) communication through cluster head selection.

Adaptive Security-aware Cone-shaped Request-zone Location-aided Routing Protocol Using Agent-Based Methodology for VANETs

In this paper, we propose an agent-based adaptive security-aware enhancement algorithm with congestion control mechanism for limited resources VANETs using intelligent transportation systems. The proposed security algorithm is to be installed at a central server that receives information about the vehicle resources from distributed intelligent road side units (RSUs) in a virtual segmented road via existing wireless cellular networks (4G). The security algorithm adaptively makes a decision about the best integrity security service level a source node can adopt while encrypting its data flows according to a resource estimation congestion control mechanism. The information about the security level decision will be transferred from the central server to the RSU via wireless cellular networks (4G). From the other side, the RSU transfers it to the source vehicle node via Wi-Fi direct technology. In finding the secure route to destination, we adopt our previously proposed secure cone-shaped request-zone LAR (SCSLAR). In order to notify the destination node with the security level that was adopted by the source, we override the 1st 3 bits of the 1st payload byte of the IEEE 802.11 wireless frame format to represent the associated integrity security level. Using three main network parameters: node density, vehicular speed, and number of malicious nodes, extensive simulation results based on QualNet simulator that is based on the GloMoSim used by Scalable Network Technologies (SNT) show that the proposed secure cone-shaped request zone LAR with resource estimation methodology (SCSLAR-RE) outperforms the SCSLAR regarding data delivery and in protecting the VANET from being congested by heavy traffic load through minimizing both the buffer consumption and the average packets delays at the destination side, while providing the best integrity security level to the data streams that make them robust against the alteration threat.

Time-Frequency analysis of acoustic signals from tyre-pavement interaction.

The acoustic responses of a dynamical system reflect the characteristics of the external excitation and vibrating system. The objective in an identification problem is to characterise the system and excitation from the measured responses. This becomes challenging if the characteristics of the dynamical system are time-varying. The analysis of the acoustic signal generated as a result of the tyre-pavement interaction (TPI) is one such situation, where in order to study the time-varying nature there is a need to obtain the characteristics of the instantaneous response. This study explores the single frequency filtering analysis of signals to examine the time-varying characteristics of tyre-pavement interaction noise (TPIN). Field measurements of TPI acoustic signals were obtained for a two-wheeler driven at different speeds on asphalt and cement concrete pavements. The time-frequency characteristics of the tyre tread impact and of the air cavity resonances are analysed by investigating the effect of vehicular speed and pavement type on TPIN. The specific features of TPIN such as the harmonics due to periodic tread impact and the frequency response due to resonances of the air cavities are examined in detail. The analysis presented in this paper will be useful in developing methods for health monitoring of tyre and pavement systems.

Neural Network-based Model for Traffic Prediction in the City of Valencia

There are many models that attempt to predict vehicular speed in urban and interurban roads, the noise pollution caused by traffic in cities, or even the traffic flow based on historical data from cameras or from people's mobile phones. Such information can be useful for administration authorities, and for researchers attempting to improve the living conditions of citizens. In this context, the aim of the present study is to design a model capable of predicting the traffic flow in the city of Valencia, Spain, based on data collected by electromagnetic loops distributed throughout the city. With a good traffic prediction, it will be possible to foresee possible traffic jams, and also to trigger countermeasures to mitigate them. Therefore, two models based on two recurrent neural networks of Long Short-Term Memory (LSTM) type have been designed to predict the traffic flow in the different streets of Valencia at the different hours of the day. We also study the influence of the specific characteristics used on the accuracy of the model. The results of our experiments show that, despite the high heterogeneity in terms of per-street traffic behaviour, it is possible to reach useful prediction models with low errors.

Mining dockless bikeshare data for insights into cyclist behavior and preferences: Evidence from the Boston region

• Studied cyclists’ behavior using en route GPS records from a dockless bikeshare system. Commercial areas have excess origins, while residential areas have excess destinations. • Cyclists often use sidewalks, other footpaths , and parking lot drives. • 36% of trips involve contraflow on a one-way segment. • Due to underdeveloped bike network, only 7% of trips are 100% on low-stress links. Emerging micromobility services provide not only new transportation options, but also valuable sources of data for researchers and planners seeking to understand traveler behavior and preferences and to improve transportation networks. Dockless systems for shared bicycles and electric scooters have recently begun operating in many American cities, providing data not only on trip start and end, but also on the route followed. In this study, we analyze data from more than 110,000 dockless bikeshare trips in Boston’s suburbs. About 15% of trip ends were within 100 m of a transit station, indicating that bikeshare serves many more functions than access to transit. More than 40% ended in a town or village center, suggesting that bikeshare may support the local economy. From examining GPS traces, it was clear that – contrary to the assumptions of common bike routing algorithms – bike riders often use sidewalks, other footways and soft-surfaced paths, parking lots, and driveways, and 36% ride the wrong way on one-way streets. An examination of the streets with high contraflow volumes indicates two common profiles. One is streets whose travel channel is so narrow that either the bike or vehicle has to pull into a parking lane to let the other pass; those streets typically have low traffic speeds and very low traffic volumes. The other is streets whose travel channel is wide enough for a bike and car to pass without either yielding to the other; they typically have greater vehicular speed and volume, but still only one lane. After classifying streets by level of traffic stress, we find that only 7 percent of trips use exclusively low-stress links, and that about 40 percent of bike-miles are ridden on low-stress links. These low percentages are consistent with an underdeveloped bike network that forces riders to use through streets for all except ultra-local trips; it may also reflect a prevalence of sidewalk riding on major arterials.

New fuel consumption model considering vehicular speed, acceleration, and jerk

A novel computational model for the volatile state was developed to improve eco-driving in intelligent transportation systems (ITS). First, the volatile state was divided into eight types using vehicle acceleration and jerk as delineating criteria. Data analysis showed that each jerk type had a different proportion and contribution level to fuel consumption. Next, the model was created by considering eight instantaneous driving decisions as represented by vehicle speed, acceleration, and jerk. The model input included vehicle speed multiplied by acceleration, with jerk as a classifier. The model was calibrated using quadratic polynomial fitting, and validated using another portion of the data. Finally, predictions were compared with the widely used Vehicle Specific Power (VSP) model and the Virginia Tech Microscopic (VT-Micro) model to evaluate model performance. The new model thoroughly captured the measured fuel consumption and provided more accurate predictions in new routes than the above-mentioned models. The mean absolute percentage error value of the new model was ∼4.9% and 3.2% lower than those of the VSP and VT-Micro models, respectively. The determinant coefficient value was up to 95.8%, which was ∼4.6% and 8.5% higher than those of the VSP and VT-Micro models, respectively.

Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Estimation of Vehicle Speed from Pavement Stress Responses Using Wireless Sensors

Vehicle speed is a crucial input for mechanistic-based design and maintenance decision making of asphalt pavement. It can be measured using direct methods and indirect methods. Direct methods, such as video camera and inductive loops, have difficultly achieving real-time data processing and have a sophisticated installation process. Indirect methods use the mechanical responses collected by the traditional mechanical sensors to estimate speed. Although effective, these traditional mechanical sensors usually have a low survival rate and require highly sophisticated installation procedures. Recently, a new type of wireless micro-electromechanical sensor (MEMS), SmartRock (Sensor Technology Research Development and Application Laboratory, Hong Kong), has been promising in pavement research to monitor contact stress and acceleration at the particle level. This paper aims to explore the potential and reliability of using the SmartRock in acquiring vehicular speed and to evaluate the effect of loading amplitude and wandering effect (i.e., the loading location relative to the sensor or monitoring-point location) on speed estimation. In this study, SmartRock sensors were embedded in a semirigid pavement, and an accelerated pavement testing (APT) facility was used to apply loadings. Vehicle speed was estimated according to the dynamic stress responses (i.e., stress waves) measured by the SmartRock. In addition, a finite-element model was developed. After being calibrated by SmartRock stress data, this model, combined with SmartRock data, was used to evaluate the effect of load amplitude and wandering on the speed estimation. Findings indicate that SmartRock can reasonably estimate the vehicle speed using both time-domain and frequency-domain methods. The loading amplitude and wandering may have little effect on the speed estimation according to the results of this study, but future work is recommended to verify such findings.

Impact of vehicular pollution on carboxyhemoglobin levels and respiratory response in urban cyclists

BACKGROUND AND AIM: The present case-crossover study evaluated changes in carboxyhemoglobin (COHb) levels and spirometry values of 37 volunteers caused by vehicular pollution on a bike path parallel to a road with a high vehicular flow in two exposure scenarios (high exposure on weekdays and low exposure on Sundays) in the city of Bogotá, Colombia METHODS: For each scenario, ultrafine particles (UFP), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon black concentrations were measured, as well as vehicular flow and speed at two points on the road of interest, to explore the possible associations among these variables and exposure to each pollutant. RESULTS:Significant differences were obtained for UFP, CO, NO2, and SO2 concentrations between the high- and low-exposure scenarios, and between the estimated exposure doses (EED) of UFP, CO, and SO2. A strong correlation was found between the EED of CO and the percentage change in COHb before and after each bike ride (r=0.7571). Concerning UFP, a greater weight of background pollution was found in the high exposure scenario, followed by the impact of meteorological variables, and emission sources along the road under study. CONCLUSIONS:To explain the variability of respiratory parameters, the only statistically significant model was obtained for peak expiratory flow (25-75%) of the forced expiratory maneuver (MMEF25-75%), having EED of CO and average UFP size as explanatory variables. Cyclists arrived at the endpoint of the ride with high COHb levels due to exposure to air pollutants along the route they used to arrive at the study site. KEYWORDS: Air pollution, Mixtures, Biomarkers of exposure, Respiratory outcomes, Short-term exposure

Revisiting Speed Management Strategies in Malaysia


 
 
 
 Speed, because of its centrality to mobility, deserves effective management approaches. This review examined the present approaches in Malaysia: speed zoning, enforcement, education, and physical engineering; before putting forward evidence of their limited effectivity. The discussion continued with a proposal for preventive approaches in managing speed. Instead of reactive measures, i.e. curbing the speeding, the proposal clutches on the elimination of room for speeding by advocating for vehicular speed management system – e.g. the intelligent speed adaptation technology, or top speed limiting by design. The potential regulatory mechanism, implementation challenges, and subsequent courses of action to realize the proposal concludes the article.
 
 
 

BIOMECHANICS OF PENETRATING TRAUMA AND INJURY MITIGATING PROTECTIVE MEASURES IN GUARDRAIL MOTOR VEHICLE CRASHES

The objective of the present study is two-fold. First, the elucidation of the biomechanics of penetrating trauma as a result of guardrail intruding into the occupant compartment. Second, the evaluation of the biomechanical efficacy of hybrid tension-compression guardrails to better protect occupants. The nine fatally guardrail penetrating crashes occurred between 2016 and 2019 were analyzed to study the mechanism of injuries. Four car-to-guardrail crash tests were conducted using a hybrid guardrail that integrated the commonly used W-beam with a new design of tension-based end terminal. The test included the impact of a bogey-type platform, small sedan vehicles, and a pick-up truck at highway speeds onto the guardrail. The impact orientation was varied to simulate the frontal and oblique corner crashes with a speed ranging from 90 to 111 kph. The real-world studies showed that the fatal injuries were due to impaling guardrail regardless of vehicular speed and size. The occupants not in the trajectory of the guardrail in the same vehicle sustained minor injuries despite experiencing a similar energy level. In these cases, the crash severity was survivable without the guardrail penetration. The mean pre-impact speed, change in speed, and vehicular acceleration was 117 kph, 20 kph, and 97 m/sec2, respectively. The hybrid guardrail system deflected the vehicle without any penetration into the occupant compartment. The mean peak accelerations in crash tests were below injurious threshold levels. The present research shows that the hybrid guardrail system not only eliminated the intrusion into occupant survival space but also deflected the vehicle.

Development of real-world emission factors for on-road vehicles from motorway tunnel measurements

Abstract In India, there is uncertainty in vehicular emission estimation due to the lack of reliable emission factors (EFs) that represent the real-world driving conditions. In this study, real-world EFs of particulate (PM2.5, PM10, PM2.5-10, PNCs, and BC) and gaseous (CO, CO2, VOCs, and NO2) pollutants from on-road vehicles were developed from the roadway tunnel measurements. Air pollutants measurement campaign was carried out at the Eastern Freeway tunnel (South-bound) in Greater Mumbai in April 2019. Particulate and gaseous samplers were deployed at the entry and exit of the tunnel to measure the targeted air pollutants covering both peak and off-peak traffic hours. The average traffic in the tunnel was ~2450 h−1, dominated by Light-Duty Vehicles (LDVs) (~98%) with an average (±SD) vehicular speed of 59 (±5) km h−1. Almost ~93% of vehicles plying inside the tunnel were with ≤10 years of age and 87% of the fleet predominantly complied with Bharat Stage-IV (BSIV) emission standards. The measured average (±SD) fleet EFs for PM2.5, PM10, PM2.5-10, BC, NO2 and VOCs were 44.2 (±26.9), 118.1 (±28.5), 69.7 (±20.1), 147.0 (±31.5) and 92.9 (±20.7) mg veh−1 km−1, respectively. For CO and CO2, it was 1.6 (±0.3) and 132.4 (±25.0) g veh−1 km−1, and 2.1 (±0.5) ( × 1013 particles veh−1 km−1) for particle number concentrations. The coarse particle EF was 1.6 folds higher than the fine particle EF, signifying the importance of non-exhaust vehicular emission sources. Compared to laboratory-based EFs, the measured real-world EFs were 1.8, 4.8, and 2.2 folds higher for PM2.5, CO, and NO2, respectively. This is likely due to the presence of aged and poorly maintained vehicles in the real-world which were not taken into consideration by the lab-based studies. Capsule We developed hourly EFs of particulate and gaseous pollutants for on-road real-world vehicular fleet (having predominantly LDVs) from air pollution measurements and traffic characterization inside a motorway tunnel in Mumbai, India. Exhaust as well as non-exhaust EFs are developed and compared with the laboratory-based re-constructed EFs (which were 2–5 fold lower than real-world EFs).

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

Disturbance to the moving traffic, such as on-street parking and bus stop on carriageway normally called as side friction in the literature, is one of the major problems that impact the vehicular speed on urban arterials in developing countries like India. Regression model to study the mode-wise vehicular speeds using side friction and other influencing variables faces the problem of multicollinearity as there is a strong correlation between the independent variables which results in high R2 and high p values. Instead of dropping the highly correlated variables which is the conventional practice to deal with multicollinearity, we proposed an approach of taking a linear combination and ratio of independent variables. To test the applicability of the proposed approach, traffic data from five locations in Hyderabad and Vellore in India having a wide variety of geometric and traffic characteristics with one or more side frictions were collected and regression models were built. It was found that the proposed approach of taking linear combination and ratio worked very well as the models exhibited a high R2, low p values and low variance inflation factor. The regression models also forecast speeds with good and reasonable accuracy. For better understanding of the impact of side friction, the present study proposed an index called side friction index and it was found that the number of side friction elements and their impact are directly proportional. Also, the impact is not uniform across the different vehicle types. Though the present study focussed on side friction problem, the approach that we proposed in this paper to deal with multicollinearity can be applied in other fields as well.

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis

Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.