Non-rush Hours Research Articles

English

National Highways of Bangladesh, where majority of fatal accident takes place, are considered as the accident-prone location. Statistics revealed that despite some major initiatives undertaken to improve road safety, death rate at National Highways of Bangladesh is quite high compared to other developed as well as developing nations. To understand the relationship between injury severity and accident factors, investigation concentrate on different influential factors are responsible for injury severity. Micro Computer Accident Analysis Packages (MAAP5), data of Bangladesh Police from 2004-2015, is used in this investigation to identify the probability of fatal and grievous injury. Preliminary analysis revealed that almost 80% accident recorded as fatal accident and majority of the accidents occurred in fair-weather condition, at straight portion of the road, in non-rush hour and in broad daylight. Conversely, three level of injury severity (dependent variables) was considered in the econometric analysis namely, fatal, grievous and simple injury. Ordered Probit Model examined the dependent variables based on several explanatory variables such as time factors, road geometry, environmental conditions, vehicle categories, driver attitudes, passenger and pedestrian behavior. Considering different level of independent variables, it is found that under the heading of collision type, hit Pedestrian is the statistically significant variable that creates severe injury. Considering vehicle characteristics, non-motorized vehicle, bus and motorcycle was found to be the most vulnerable group of road users in Bangladesh and the probability of fatal injury is much higher for vehicle with multiple defects. This investigation also pointed on some remedial measures that will help the decision maker to set up long as well as short term strategies. For instance, installation of physical barrier at the accident-prone location will minimize the pedestrian fatalities and isolation of non-motorized vehicle and motorcyclist from main carriageway, which would dramatically reduce injury severity on highways. Finally, this investigation also provides some specific guideline for future steps on injury severity analysis in Highways of Bangladesh. Key words: National Highway, Fatal Accident, Ordered Probit Model, Variables

Impact of transient truck and train traffic on ambient air and noise levels in underserved communities

Traffic-related air and noise pollution on or near major roadways have been examined but these pollutants have not been extensively investigated away from major roadways in residential communities, especially in the United States. To evaluate the impact of trucks and trains passing nearby on air and noise pollution in residential areas during non-rush hours, we simultaneously measured concentrations of size-resolved airborne particulate matter (PM) and sound pressure levels as A-weighted equivalent (dBA) with frequencies in three underserved communities adjacent to industrial facilities in Houston, TX. We found that median concentrations for PM1 (particle size ≤ 1 µm) and PM10 (particle size ≤ 10 µm) were highest when trucks passed by at sampling locations, followed by periods when trains passed by. PM1 and PM10 concentrations were lowest at background (defined when there was no truck or train traffic near the monitoring location). Median PM2.5 (particle size ≤ 2.5 µm) mass concentrations were 19.8 µg/m3 (trains), 16.5 µg/m3 (trucks), and 13.9 µg/m3 (background). Short-term increases in noise were attributed to trains and trucks passing nearby as well. The median noise levels were the highest when trains passed by (66.7 dBA) followed by periods when trucks were in the vicinity of the monitoring locations (62.5 dBA); background levels were 58.2 dBA. The overall Spearman correlation coefficients between air and noise pollution were between 0.09 and 0.46. Hence, we recommend that both air pollutant and noise levels be concurrently evaluated for accurate exposure assessment related to traffic sources in residential communities.

The impact of seating location on black carbon exposure in public transit buses: Implications for vulnerable groups

This study assesses the differences in personal exposure to black carbon (BC) in the cabin of modern public buses in Stuttgart, Germany. We measured BC concentrations in four different seating locations, including priority seating locations, during rush hour and non-rush hour periods, and determined average exposures as well as the frequency of peak exposures. We hypothesize that the elderly and children (known to be more vulnerable to health impacts of air pollution) are exposed to higher concentrations as they are encouraged to make use of priority areas in proximity to doors in the middle of the bus, allowing for a greater flux of pollutants. We found no statistically significant difference in the mean BC concentrations between priority seating areas and other locations. However, a significant increase (+32%) in the frequency of BC peaks (‘spikes’) was observed in one priority seating area when compared to the back of the bus. Furthermore, we found that travelling during rush hour was associated with significantly higher average in-vehicle BC concentrations in all seating locations compared with off peak hours (1122 ng/m3 or 38% higher), as well as a greater magnitude of the largest concentration spike of each trip 1295 ng/m3 (38%). Further work may be necessary to refine most appropriate location for priority seating areas in buses and bus stops.

Keeping pace with the ebbs and flows in daily nursing home operations.

Nursing homes are challenged to develop staffing strategies that enable them to efficiently meet the healthcare demand of their residents. In this study, we investigate how demand for care and support fluctuates over time and during the course of a day, using demand data from three independent nursing home departments of a single Dutch nursing home. This demand data is used as input for an optimization model that provides optimal staffing patterns across the day. For the optimization we use a Lindley-type equation and techniques from stochastic optimization to formulate a Mixed-Integer Linear Programming (MILP) model. The impact of both the current and proposed staffing patterns, in terms of waiting time and service level, are investigated. The results show substantial improvements for all three departments both in terms of average waiting time as well as in 15 minutes service level. Especially waiting during rush hours is significantly reduced, whereas there is only a slight increase in waiting time during non-rush hours.

Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application.

The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient's home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution's catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29min during non-rush hour (p = 0.03), and 28.80min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.

Ultrafine particle number concentrations and size distributions around an elevated highway viaduct

High-traffic roadways substantially contribute to UFP number concentrations and size distributions within many urban areas and may contribute to excess urban mortality and morbidity. A number of previous studies have quantified near-road concentrations of UFP and the decay with horizontal displacement from highways and indicated rapid decreases to near background levels within 300 m of the line-source. However, the majority of those previous studies have focused on ground-level highways and so herein we present data from sampling perpendicular to an elevated highway viaduct. Measurements taken during morning rush hour and non-rush hour periods using a bicycle-mounted TSI Nanoscan scanning mobility particle sizer within distances of 200–300 m from the freeway are lower than those from previous studies of ground-level highways, but the decay of UFP number concentrations with distance from the highway is more gradual. A linear mixed-effects model indicates only distance from I-81 and air temperature are significant predictors of near I-81 UFP concentrations, but wind speed and direction and vehicle counts on the street where the measurements were conducted exhibit the expected sign of dependence.

On the density estimation of air pollution in Beijing

We apply both the kernel method and the k-nearest neighbor (k-nn) method to estimate the density of air pollutant PM2.5 in Beijing. We find that the k-nn method accommodates the data better and delivers a more reasonable density estimate than the kernel method. Then we compare the density estimates between summer and winter, rush and non-rush hours. Results suggest that the air pollution is more serious in winter and rush hours.

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour.

The impact of rainfall on the temporal and spatial distribution of taxi passengers.

This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities.

Exposure to coarse and fine particulate matter at and around major intra-urban traffic intersections of Ilorin metropolis, Nigeria

This study aims to determine the seasonal variations and composition of suspended particulate matter in different sizes PM1.0, PM2.5, PM10 and the total suspended particles (TSP) emitted at major intra-urban traffic intersections (TIs) of Ilorin metropolis. The concentration levels of PM (PM1.0, PM2.5, PM10) obtained at the TIs during the rush hours (45.1, 77.9, and 513 μg/m3) are higher than the levels obtained for the non-rush hour periods (42.3, 62.7, and 390 μg/m3). The average on-road respiratory deposition dose (RDD) rates of PM1.0, PM2.5 and PM10 during the dry period at TIs types was found to be about 24%, 9% and 25% higher than those obtained during the wet period. Based on EF values calculated, Pb and Zn were anthropogenically-derived while Fe, Mn, Cr, Cu and Mg were of crustal source. Principal component analysis (PCA) has been applied to a set of PM data in order to determine the contribution of different sources. It was found that the main principal factors extracted from particulate emission data were related to exhaust and non-exhaust emissions such as tyre wears, oil and fuel combustion sources.

Prosocial Behavior or Bystander Effect? The Role of Virtual Crowdedness in Encouraging User Contribution of a Mobile Virtual Community

We examine content creation in a geo-segmented, crowdsourced social mobile virtual community app, Waze. We conceptualize a virtual and spatial factor, virtual crowdedness (defined as the density of Waze users in a particular geospatial location) and examine its role in encouraging user contribution. We posit that the relationship between virtual crowdedness and user contribution is driven by the tension between audience effects and bystander/content saturation effects. We analyze a panel dataset of user contributions on Waze from New York City to test our hypotheses. First, our findings indicate that although virtual crowdedness has a positive influence on total number of contributions, the magnitude of the influence decreases as virtual crowdedness increases. Second, the concave-down, increasing relationship is more pronounced for rush hours with high physical crowdedness than for non-rush hours with low physical crowdedness. A variety of robustness checks and alternative analyses based on matching estimators and spatial econometric models further support the main conclusions while mitigating concerns about endogeneity and spatial autocorrelation. Our findings provide several key practical implications for platform designers in that they should allow for users to visualize density of usage as well as improve the design of social features for encouraging user contribution to the mobile virtual community.

A Real-Time Taxicab Recommendation System Using Big Trajectories Data

Carpooling is becoming a more and more significant traffic choice, because it can provide additional service options, ease traffic congestion, and reduce total vehicle exhaust emissions. Although some recommendation systems have proposed taxicab carpooling services recently, they cannot fully utilize and understand the known information and essence of carpooling. This study proposes a novel recommendation algorithm, which provides either a vacant or an occupied taxicab in response to a passenger’s request, called VOT. VOT recommends the closest vacant taxicab to passengers. Otherwise, VOT infers destinations of occupied taxicabs by similarity comparison and clustering algorithms and then recommends the occupied taxicab heading to a close destination to passengers. Using an efficient large data-processing framework, Spark, we greatly improve the efficiency of large data processing. This study evaluates VOT with a real-world dataset that contains 14747 taxicabs’ GPS data. Results show that the ratio of range (between forecasted and actual destinations) of less than 900 M can reach 90.29%. The total mileage to deliver all passengers is significantly reduced (47.84% on average). Specifically, the reduced total mileage of nonrush hours outperforms other systems by 35%. VOT and others have similar performances in actual detour ratio, even better in rush hours.

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics.

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LAeq24h, Lnight, and Lden to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LAeq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R2) was 0.68, 0.59, and 0.69 for LAeq24h, Lnight, and Lden, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LAeq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.

Timetable Optimization of Rail Transit Loop Line with Transfer Coordination

In an urban rail transit system, it is important to coordinate the timetable of a loop line with its connecting lines so as to reduce the waiting time of passengers. This is particularly essential because transfer passengers usually account for the majority of the total passengers in loop lines. In this paper, a timetable optimization model is developed for loop line in order to minimize the average waiting time of access passengers and transfer passengers. This is performed by adjusting the headways and dwell times of trains on the loop line. A genetic algorithm is applied to solve the proposed model, and a numerical example is used to verify its effectiveness. Finally, a case study of a loop line in the Beijing urban rail transit system is conducted. Waiting times of the passengers and the number of waiting passengers are used as performance indicators to verify the optimization results in rush hours and non-rush hours. The results show that the average waiting times for the up-track and down-track are reduced by 3.69% and 2.89% during rush hours and by 11.60% and 11.47% during non-rush hours, respectively.

Particulate Matters Levels in Subway Tunnels and Cabins

This research aims to discern the characteristics of PM₁₀ concentrations measured in subway cabins, tunnels and outdoors at the Seoul Metropolitan subways. PM₁₀ concentrations in cabins were found between the range of 45.3 and 101.9μg/m³, and in tunnels the ranges were between 155.3 and 230.6μg/m³. PM₁₀ concentrations in cabins and tunnels during rush hours were 16.5% and 3.8% respectively higher than non-rush hours. Fine particles were higher than that of coarse particles both in subway cabins and tunnels.

Spatial and temporal variability of VOCs and its source estimation during rush/non-rush hours in ambient air of Delhi, India

The present study deals with the concentrations of individual and total volatile organic compounds (TVOCs) present in the ambient atmosphere of Delhi. Sampling was done in four different zones, viz. residential, commercial, industrial and heavy traffic density area (HTDA) during rush and non-rush hours. The National Institute for Occupational Safety and Health (NIOSH)-1501 standard method was used for benzene, toluene, ethylbenzene and xylene (BTEX) measurements while real-time monitoring was done for TVOC using a data-logging photoionization detector. Results showed that the mean concentrations of TVOC and ∑BTEX were found to be highest in HTDA as compared to those in other zones. Toluene contributed the most among BTEX as 28.5, 108.6, 129.6 and 104.5 μg/m3 at residential, commercial, industrial and HTDA, respectively. However, the concentrations of TVOC were found to be 518.9 μg/m3 in HTDA followed by industrial (511.3 μg/m3), commercial (429.6 μg/m3) and residential areas (199.5 μg/m3). It is noted that the concentrations of the pollutants were observed to be higher during rush hours in contrast to non-rush hours due to more number of vehicles plying the road. After examining the seasonal variability, the winter samples showed the highest concentrations of the pollutants which could be due to stable environment. In order to identify the sources, characteristic ratios and correlation analysis were also done. Cancer and non-cancer risks were estimated for BTEX using US Environmental Protection Agency (USEPA) guidelines. The estimated lifetime cancer risk (LCR) in this study exceeded the value of 1.0 × 10−6 recommended by the USEPA for adults and children in all zones. The population groups are in the category of “possible risk” after comparing with the Sexton’s classifications of cancer risk.

Comparing carbon dioxide levels within urban transport microenvironments during rush hour and non-rush traffic

&#x0D; Introduction: Commuters spend countless hours within tightly confined spaces with limited ventilation that may be filled with many contaminants. By analyzing if there is a significant difference between levels of carbon dioxide between rush and non-rush hour conditions, it can be determined if some commuters are subjected to poorer levels of air quality during certain times of the day. Objectives: The primary objective of this study is to understand whether there are significant ventilation deficiencies during rush compared to non-rush hour times in urban transport microenvironments. Methods: Analysis of urban transport microenvironments was done using the TSI brand QTrak Indoor Air Quality (IAQ) Monitor to gather data on carbon dioxide at 1-minute intervals on the 99 B-line express bus that runs between Broadway and Commercial Skytrain Station and the Broadway and Cambie Street Skytrain Station. Results: A one tailed T-test was done on the NCSS 9 statistical software to compare if rush hour urban microenvironments had significantly higher concentrations of carbon dioxide than when compared to non-rush hour. Statistical analysis determined that since the P-value was well above the alpha level of 0.05 (i.e. P&lt;0.05), it gives reason to accept the null hypothesis, which states that rush hour concentrations were not higher than non-rush hour. Conclusion: Statistical analysis determined that the overall concentrations of carbon dioxide during rush hour were not significantly higher than non-rush hour times. This result may have been attributed to conditions and factors during data collection that could not be controlled by the researcher. Due to the length of the route, exposure times were found to be within time-weighted averages as set out by the American Conference of Industrial Hygienists (ACGIH), though it was still not within the recommended limit of 1000 ppm as set out by the American Society of Heating, Refrigeration and Air-Conditioning (ASHRAE).&#x0D;

Land-use regression modelling of highway ultrafine particle number concentrations

Ultrafine particles (UFP; diameter < 100 nm) are suspended atmospheric solids of concern to human health due to their small diameters. Vehicles emit significant numbers of UFP, leading to steep spatial gradients on, and near, high traffic roadways. Particle number concentrations were measured while driving a cyclic route on four Toronto highways during non-rush hour weekday periods during the summer. These data were averaged over roadway length segments to create a concentration map of the route. Road segment-averaged concentrations were bimodal, and ranged from 26,000 to 106,500 particles cm-3, which is significantly greater than the average background downtown Toronto concentration (15,500 cm-3). A land-use regression model incorporating diesel truck traffic, six land use types, and distance to Lake Ontario, accounted for 34% of the variability of on-road concentrations. Understanding the complex spatial distribution of urban UFP is integral to designing epidemiological studies and understanding commute...

Spatio-temporal variation of urban ultrafine particle number concentrations

Methods are needed to characterize short-term exposure to ultrafine particle number concentrations (UFP) for epidemiological studies on the health effects of traffic-related UFP. Our aims were to assess season-specific spatial variation of short-term (20-min) UFP within the city of Basel, Switzerland, and to develop hybrid models for predicting short-term median and mean UFP levels on sidewalks. We collected measurements of UFP for periods of 20 min (MiniDiSC particle counter) and determined traffic volume along sidewalks at 60 locations across the city, during non-rush hours in three seasons. For each monitoring location, detailed spatial characteristics were locally recorded and potential predictor variables were derived from geographic information systems (GIS). We built multivariate regression models to predict local UFP, using concurrent UFP levels measured at a suburban background station, and combinations of meteorological, temporal, GIS and observed site characteristic variables. For a subset of sites, we assessed the relationship between UFP measured on the sidewalk and at the nearby residence (i.e., home outdoor exposure on e.g. balconies). The average median 20-min UFP levels at street and urban background sites were 14,700 ± 9100 particles cm−3 and 9900 ± 8600 particles cm−3, respectively, with the highest levels occurring in winter and the lowest in summer. The most important predictor for all models was the suburban background UFP concentration, explaining 50% and 38% of the variability of the median and mean, respectively. While the models with GIS-derived variables (R2 = 0.61) or observed site characteristics (R2 = 0.63) predicted median UFP levels equally well, mean UFP predictions using only site characteristic variables (R2 = 0.62) showed a better fit than models using only GIS variables (R2 = 0.55). The best model performance was obtained by using a combination of GIS-derived variables and locally observed site characteristics (median: R2 = 0.66; mean: R2 = 0.65). The 20-min UFP concentrations measured at the sidewalk were strongly related (R2 = 0.8) to the concurrent 20-min residential UFP levels nearby. Our results indicate that median UFP can be moderately predicted by means of a suburban background site and GIS-derived traffic and land use variables. In areas and regions where large-scale GIS data are not available, the spatial distribution of traffic-related UFP may be assessed reasonably well by collecting on-site short-term traffic and land-use data.

Stationary tailgating in Gaborone, Botswana: the influence of gender, time of day, type of vehicle and presence of traffic officer

This study investigated stationary tailgating in Gaborone, Botswana. We observed and measured the distance between 722 vehicles (541 male drivers, 181 female drivers) stopped at three traffic intersections in Gaborone during rush-hour and non-rush hour. Gender of driver, type of vehicle (private, commercial, government or company), whether it was rush-hour or not, whether or not a traffic officer was present and distance from the car in front, were recorded. Based on a benchmark of 350 centimetres, derived from the recommendation that cars maintain the distance of the length of a car from the vehicle in front of them when stopped, it was found that 76% of drivers tailgated. In general, men tailgated more than women and tailgating occurred more during rush-hour and when there was a traffic officer present. In addition, a series of four-way analysis of variance tests yielded a main effect for presence of officer, such that the average distance kept from the car in front was significantly less when a traffic officer was present than when a traffic officer was not present. The main effects of gender, time of day and vehicle type were not significant. Findings are discussed in relation to the impact on pedestrians as well as drivers and implications for traffic regulation procedures.