Potential of two-dimensional electro-fluid-dynamic devices for continuous purification of multiple components from complex samples

Two-dimensional electro-fluid-dynamic (EFD) devices, in which both electric field and hydrodynamic pressure are used to drive the analyte and fluid migration, enable two-dimensional channel networks to be used for chemical separation instead of one-dimensional column separation systems. Investigation of the theory of mass transfer in symmetrical Y-shaped EFD devices shows that the magnitude of pressure-induced velocity in lateral channels at critical boundary conditions between different steady state migration paths is independent of the channel cross-sectional area ratio. Therefore, the analyte has four possible mass transfer pathways according to the electric field and pressure setup in all symmetrical Y-shaped 2-D EFD devices, and such devices with any cross-sectional area ratio have the capacity to continuously purify two analytes from a mixture simultaneously. In addition, a new format of multiple-branched 2-D EFD devices is introduced to process multiple analytes. A "proof-reading" mechanism based on the infinite resolution conditions ensures the purity of the components collected. The separation processes are simulated by COMSOL Multiphysics, and the migration behavior of the analytes was monitored using fluorescent dyes to verify the flow behavior of different analytes in individual channels. These 2-D EFD devices offer the potential of continuous fractionation and purification of analytes from complex sample mixtures.     

Evaluation of bike boxes at signalized intersections

This paper presents a before-after study of bike boxes at 10 signalized intersections in Portland, Oregon. The bike boxes, also known as advanced stop lines or advanced stop boxes, were installed to increase visibility of cyclists and reduce conflicts between motor vehicles and cyclists, particularly in potential "right-hook" situations. Before and after video were analyzed for seven intersections with green bike boxes, three intersections with uncolored bike boxes, and two control intersections. User perceptions were measured through surveys of cyclists passing through five of the bike box intersections and of motorists working downtown, where the boxes were concentrated. Both the observations and survey of motorists found a high rate of compliance and understanding of the markings. Overall, 73% of the stopping motor vehicles did not encroach at all into the bike box. Both motor vehicle and bicycle encroachment in the pedestrian crosswalk fell significantly at the bike box locations compared to the control intersections. The bike boxes had mixed effects on the motorists' encroachment in the bicycle lane. The number of observed conflicts at the bike box locations decreased, while the total number of cyclists and motor vehicles turning right increased. Negative-binomial models based upon the data predict fewer conflicts with the boxes, particularly as right-turning motor vehicle volumes increase. Observations of yielding behavior at two bike box and one control intersection found an improvement in motorists yielding to cyclists at the bike box locations. Differences in the traffic volumes and location contexts make firm conclusions about the effects of green coloring of the boxes difficult. Higher shares of surveyed motorists felt that the bike boxes made driving safer rather than more dangerous, even when the sample was narrowed to respondents who were not also cyclists. Over three-quarters of the surveyed cyclists thought that the boxes made the intersection safer.     

Modeling aggressive driver behavior at unsignalized intersections

The processing of vehicles at unsignalized intersections is a complex and highly interactive process, whereby each driver makes individual decisions about when, where, and how to complete the required maneuver, subject to his perceptions of distances, velocities, and own car's performance. Typically, the performance of priority-unsignalized intersections has been modeled with probabilistic approaches that consider the distribution of gaps in the major-traffic stream and their acceptance by the drivers of minor street vehicles based on the driver's "critical gap". This paper investigates the aggressive behavior of minor street vehicles at intersections that are priority-unsignalized but operate with little respect of control measures. The objective is to formulate a behavioral model that predicts the probability that a driver performs an aggressive maneuver as a function of a set of driver and traffic attributes. Parameters that were tested and modeled include driver characteristics (gender and age), car characteristics (performance and model year), and traffic attributes (number of rejected gaps, total waiting time at head of queue, and major-traffic speed). Binary probit models are developed and tested, based on a collected data set from an unsignalized intersection in the city of Beirut, to determine which of the studied variables are statistically significant in determining the aggressiveness of a specific driver. Primary conclusions reveal that age, car performance, and average speed on the major road are the major determinants of aggressive behavior. Another striking conclusion is that the total waiting time of the driver while waiting for an acceptable gap is of little significance in incurring the "forcing" behavior. The obtained model is incorporated in a simple simulation framework that reflects driver behavior and traffic stream interactions in estimating delay and conflict measures at unsignalized intersections. The simulation results were then compared against real world observations, representing an important step in validating the model of aggressive driver behavior at unsignalized intersections.     

Surface condition and safety at signalised intersections

The study reported herein focuses on assessing the effects and contributions of pavement surface condition parameters including roughness, rut depth and skid resistance on crash frequencies and rates at signalised intersections. The assessment has been performed using graphical trend analysis and negative binomial regression. For each signalised intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of 3 and maximum of 5 years before and after treatment years. Analyses results show that before treatment, light condition (day/night), road surface moisture condition (wet/dry) and skid resistance are the significant contributors to crash occurrence. For after-treatment; light condition, road surface moisture condition and the interaction between roughness and traffic volume are the significant contributors. The study aim and approach are presented herein together with a discussion of the main findings.     

Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida

This study analyzes vehicle-pedestrian crashes at intersections in Florida over 4 years, 1999-2002. The study identifies the group of drivers and pedestrians, and traffic and environmental characteristics that are correlated with high pedestrian crashes using log-linear models. The study also estimates the likelihood of pedestrian injury severity when pedestrians are involved in crashes using an ordered probit model. To better reflect pedestrian crash risk, a logical measure of exposure is developed using the information on individual walking trips in the household travel survey. Lastly, the impact of average traffic volume on pedestrian crashes is examined. As a result of the analysis, it was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes. Higher average traffic volume at intersections increases the number of pedestrian crashes; however, the rate of increase is steeper at lower values of average traffic volume. Based on the findings in the analysis, some countermeasures are recommended to improve pedestrian safety.     

Analysis of drivers' preparatory behaviour before turning at intersections

This study analyses naturalistic driving behaviour before making turns at intersections. Instrumented vehicles recorded drivers? preparatory behaviour, including moving the right foot to cover the brake pedal and activating the turn-signal, while approaching the turning point on public roads. Data were analysed for relations between driver preparations, different traffic conditions (with or without forward and/or following vehicles) and the intersection type and turning direction. The results suggest that braking began closer to the target intersection when driving with a vehicle in front, independent of the number of traffic lanes or the turning direction. In contrast, the onset of braking occurred further before the intersection when drivers had a forward vehicle while approaching intersections after curves, where drivers cannot readily determine the distance between their vehicle and the turning point until just before the intersection. The presentation timing of vehicle navigation systems for each traffic condition is discussed based on the road structures of turning points.     

Safety evaluation of flashing amber operation at signalized intersections

The safety of a flashing amber signal for all directions at off-peak hours as a replacement for regular traffic signal operations was evaluated. The proposed control strategy was motivated by the need for energy conservation through reduced amounts of acceleration and of idling time of vehicles. The methodology of the study employed as a measure a broadened definition of "conflict" that freed observers from the need to detect only emergency evasive manoeuvres and decreased their subjective interpretations. Observations were carried out by trained observers at a sample of intersections, using two control strategies: full signal operation and flashing amber phase. Stationed at each leg of an intersection, the observers noted the travel direction of any two vehicles involved in a conflict. The results showed that the most frequent type of conflict under full signal operation was of the rear-end type; during the flashing amber operation, crossing and merging conflicts were dominant. It was concluded that up to a volume of 600 vehicles per hour, flashing operation does not increase the number of conflicts.     

Analysis of traffic accidents at urban intersections in Riyadh

Previous studies have shown that intersection-related accidents account for about 50% of all accidents registered annually in Riyadh, the capital of the Kingdom of Saudi Arabia (KSA). More than half of these accidents are classified as severe. In this study, an attempt was made to investigate traffic accidents that occurred at both intersections and non-intersection sites. The goal was to analyze the nature of such accidents to determine their characteristics so that remedies could be sought or at least future research could be suggested. For this purpose, a sample of 1774 reported accidents was collected in a systematic random manner for the period 1996-1998 (651 severe accidents (accidents resulting in at least one personal injury or fatality) and 1123 property-damage-only (PDO) accidents). Conditional probability and contingency table analyses were used to make inferences from the data. The study found that improper driving behavior is the primary cause of accidents at signalized urban intersections in Riyadh; running a red light and failing to yield are the primary contributing causes. The analysis indicates that there is an urgent need to review existing intersection geometry along with the traffic control devices installed at these sites. In addition, public education campaigns and law enforcement strategies are urgently needed.     

Aggressiveness propensity index for driving behavior at signalized intersections

The development of a quantitative intersection aggressiveness propensity index (API) is described in this paper. The index is intended to capture the overall propensity for aggressive driving to be experienced at a given signalized intersection. The index is a latent quantity that can be estimated from observed environmental, situational and driving behavior variables using structural equations modeling techniques. An empirical study of 10 major signalized intersections in the greater Washington DC metropolitan area was conducted to illustrate the approach. The API is shown to provide (a) an approach for capturing and quantifying aggressive driving behavior given certain measurements taken at a particular intersection, (b) understanding of the factors and intersection characteristics that may affect aggressiveness, and (c) an index for the cross comparison of different traffic areas with different features. This index has the potential to support safety policy analysis and decision-making.     

Driver behaviour and accident records at unsignalized urban intersections

The main purpose of the study was to investigate whether Stop signs differ from Yield signs in terms of their associated accident rates, and whether or not an increase in level or traffic control is necessarily beneficial to traffic safety at all hazardous urban intersections. The study is based on a "before" and "after" evaluation of accident rates at several unsignalized intersections, where the level of control was changed because of their accident history. It was found that any increase in the level of control at such intersections tended to cause more vehicle accidents and fewer pedestrian accidents, although most of the changes were statistically insignificant. Several possible explanations to the findings are evaluated and discussed. It is concluded that increasing the control level at unsignalized intersections will not necessarily result in an overall reduction in accidents.     

Turning-lane and signal optimization at intersections with multiple objectives

Traffic congestion at intersections is a serious problem in cities. In order to discharge turning vehicles efficiently at intersections to relieve traffic jams, multiple left-turn and right-turn lanes are often used. This article proposes a novel multi-objective optimization method for signal setting and multiple turning-lane assignment at intersections based on microscopic traffic simulations and a cell-mapping method. Vehicle conflicts and pedestrian interference are considered. The intersection multi-objective optimization problem (MOP) is formulated. The cell-mapping method is adopted to solve the MOP. Three measures of traffic performance are studied including transportation efficiency, energy consumption and road safety. The influence of turning-lane assignment on intersection performance is investigated in the optimization. Significant impacts of the number of turning lanes on the traffic are observed. An algorithm is proposed to assist traffic engineers to select and implement the optimal designs. In general, more turning lanes help increase turning traffic efficiency and lower fuel consumption in most cases. Remarkable improvement in traffic performance can be achieved with combined optimization of lane assignment and signal setting, which cannot be obtained with signal setting optimization alone. The studies reported in this article provide general guidance for intersection planning and operation. The proposed optimization methodology represents a promising emerging technology for traffic applications.     

Temporal and spatial analyses of rear-end crashes at signalized intersections

In this study, the generalized estimating equations with the negative binomial link function were used to model rear-end crash frequencies at signalized intersections to account for the temporal or spatial correlation among the data. The longitudinal data for 208 signalized intersections over 3 years and the spatially correlated data for 476 signalized intersections which are located along different corridors were collected in the state of Florida. The modeling results showed that there are high correlations between the longitudinal or spatially correlated rear-end crashes. Some intersection related variables are identified as significantly influencing rear-end crash occurrences at signalized intersections. Intersections with heavy traffic on the major and minor roadways, having more right and left-turn lanes on the major roadway, having a large number of phases per cycle (indicated by the left-turn protection on the minor roadway), with high speed limits on the major roadway, and in high population areas are correlated with high rear-end crash frequencies. On the other hand, intersections with three legs, having channelized or exclusive right-turn lanes on the minor roadway, with protected left-turning on the major roadway, with medians on the minor roadway, and having longer signal spacing have a lower frequency of rear-end crashes.     

Landscaping of highway medians and roadway safety at unsignalized intersections

Well-planted and maintained landscaping can help reduce driving stress, provide better visual quality, and decrease over speeding, thus improving roadway safety. Florida Department of Transportation (FDOT) Standard Index (SI-546) is one of the more demanding standards in the U.S. for landscaping design criteria at highway medians near intersections. The purposes of this study were to (1) empirically evaluate the safety results of SI-546 at unsignalized intersections and (2) quantify the impacts of geometrics, traffic, and landscaping design features on total crashes and injury plus fatal crashes. The studied unsignalized intersections were divided into (1) those without median trees near intersections, (2) those with median trees near intersections that were compliant with SI-546, and (3) those with median trees near intersections that were non-compliant with SI-546. A total of 72 intersections were selected, for which five-year crash data from 2006–2010 were collected.The sites that were compliant with SI-546 showed the best safety performance in terms of the lowest crash counts and crash rates. Four crash predictive models—two for total crashes and two for injury crashes—were developed. The results indicated that improperly planted and maintained median trees near highway intersections can increase the total number of crashes and injury plus fatal crashes at a 90% confidence level; no significant difference could be found in crash rates between sites that were compliant with SI-546 and sites without trees. All other conditions remaining the same, an intersection with trees that was not compliant with SI-546 had 63% more crashes and almost doubled injury plus fatal crashes than those at intersections without trees. The study indicates that appropriate landscaping in highway medians near intersections can be an engineering technology that not only improves roadway environmental quality but also maintains intersection safety.     

The safety implications of some control changes at urban intersections

Injury accident data for a sample of New Zealand urban intersections were analysed, looking for an increase or decrease which coincided with a change in control. Increases or decreases were also looked for in certain types of accidents and in accident severity. The control changes considered were from give way signs, stop signs and no control to signals and small roundabouts and also from no control to give way and stop signs.     

Modeling left-turn crash occurrence at signalized intersections by conflicting patterns

In order to better understand the underlying crash mechanisms, left-turn crashes occurring at 197 four-legged signalized intersections over 6 years were classified into nine patterns based on vehicle maneuvers and then were assigned to intersection approaches. Crash frequency of each pattern was modeled at the approach level by mainly using Generalized Estimating Equations (GEE) with the Negative Binomial as the link function to account for the correlation among the crash data. GEE with a binomial logit link function was also applied for patterns with fewer crashes. The Cumulative Residuals test shows that, for correlated left-turn crashes, GEE models usually outperformed basic Negative Binomial models. The estimation results show that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns. For example, for each pattern, the traffic flows to which the colliding vehicles belong are identified to be significant. The width of the crossing distance (represented by the number of through lanes on the opposing approach of the left-turning traffic) is associated with more left-turn traffic colliding with opposing through traffic (Pattern 5), but with less left-turning traffic colliding with near-side crossing through traffic (Pattern 8). The safety effectiveness of the left-turning signal is not consistent for different crash patterns; "protected" phasing is correlated with fewer Pattern 5 crashes, but with more Pattern 8 crashes. The study indicates that in order to develop efficient countermeasures for left-turn crashes and improve safety at signalized intersections, left-turn crashes should be considered in different patterns.     

Analyzing angle crashes at unsignalized intersections using machine learning techniques

A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles’ angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida. Two models were estimated for angle crash frequency at 3- and 4-legged unsignalized intersections. Treating crash frequency as a continuous response variable for fitting a MARS model was also examined by considering the natural logarithm of the crash frequency. Finally, combining MARS with another machine learning technique (random forest) was explored and discussed. The fitted NB angle crash models showed several significant factors that contribute to angle crash occurrence at unsignalized intersections such as, traffic volume on the major road, the upstream distance to the nearest signalized intersection, the distance between successive unsignalized intersections, median type on the major approach, percentage of trucks on the major approach, size of the intersection and the geographic location within the state. Based on the mean square prediction error (MSPE) assessment criterion, MARS outperformed the corresponding NB models. Also, using MARS for predicting continuous response variables yielded more favorable results than predicting discrete response variables. The generated MARS models showed the most promising results after screening the covariates using random forest. Based on the results of this study, MARS is recommended as an efficient technique for predicting crashes at unsignalized intersections (angle crashes in this study).     

Road factors and bicycle-motor vehicle crashes at unsignalized priority intersections

In this study, the safety of cyclists at unsignalized priority intersections within built-up areas is investigated. The study focuses on the link between the characteristics of priority intersection design and bicycle–motor vehicle (BMV) crashes. Across 540 intersections that are involved in the study, the police recorded 339 failure-to-yield crashes with cyclists in four years. These BMV crashes are classified into two types based on the movements of the involved motorists and cyclists:

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  type I: through bicycle related collisions where the cyclist has right of way (i.e. bicycle on the priority road);

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  type II: through motor vehicle related collisions where the motorist has right of way (i.e. motorist on the priority road).

The probability of each crash type was related to its relative flows and to independent variables using negative binomial regression. The results show that more type I crashes occur at intersections with two-way bicycle tracks, well marked, and reddish coloured bicycle crossings. Type I crashes are negatively related to the presence of raised bicycle crossings (e.g. on a speed hump) and other speed reducing measures. The accident probability is also decreased at intersections where the cycle track approaches are deflected between 2 and 5 m away from the main carriageway. No significant relationships are found between type II crashes and road factors such as the presence of a raised median.     

Calibration and validation of simulated vehicle safety performance at signalized intersections

A systematic procedure is presented for calibrating and validating a microscopic model of safety performance. The context in the model application is the potential for rear-end crashes at signalized intersections. VISSIM^® v.4.3 provides the simulation platform for estimating the safety performance for individual vehicles and has been calibrated and validated using separate samples of observed vehicle tracking data extracted from the FHWA/NGSIM program. The calibration exercise involves four sequential steps: (1) heuristic selection of initial model inputs, (2) statistical screening using a Plackett–Burnman design, (3) fractional factorial analysis relating inputs to safety performance, and (4) genetic algorithm procedure for obtaining best estimate input values. Three measures of safety performance were considered: crash potential index, number of vehicles in conflict and total conflict duration per vehicle. Model consistency was assessed by comparing simulated and observed safety performance based on a separate validation sample of vehicle tracking data. The suggested procedure was found to effectively estimate model input parameters that closely matched safety performance measures in the observed validation data. This procedure yields an objective and efficient means for simulation model calibration applied for estimating safety performance at signalized intersections.