Urban Roadways Research Articles

The influence of urban road traffic on nanoparticles: Roadside measurements

Abstract Nanoparticles generated from major roadways have a pronounced effect on human health, responsible for cardiovascular and respiratory problems, among others. In order to examine the effects of nanoparticles on human health, it is essential to know the factors/sources influencing both particle number concentration (PNC) and particle number distribution (PND). Real-world measurements were conducted close to an urban road located in the city of Bron near Lyon (France) over a flat terrain, where the dominant dispersion mechanisms stemmed from traffic and atmospheric turbulence. Roadside particle number concentration measurements were obtained using the Fast Mobility Particle Sizer™ (FMPS™) Spectrometer (Model 3091) at two different heights, as well as at different distances between source and sampling sites, ranging from 6.60 m to 330 m. Particles in the 5.6–600 nm size range were measured alongside a busy urban roadway. The effect of traffic volume, wind speed and direction, height above the road surface and distance from the source on both PNC and PND has been investigated. Results revealed that PNC increases significantly with an increase in height of less than 2.82 m above the road surface. Moreover, distance from the source and traffic volume is two correlated parameters. It has been shown that the highest concentration was observed at the distance nearest the road and vice versa. A bimodal daily average PND was found, with concentrated modes at 10.75 nm and then between 22.07 and 52.33 nm. Furthermore, PND was primarily influenced by nucleation and Aitken modes, as explanatory factors at the sampling site for the pollution produced from urban traffic.

A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.

Demonstrative Case of a Pedestrian Network Design Model Considering Effects on Motorized Traffic

The transformation of urban roadways into pedestrian streets is a popular measure for reshaping city parts and enhancing their livability. Nevertheless, pedestrianization schemes are expected to have some impact on the performance of the neighboring road network, especially if these are established ad-hoc or solely based on non-transport criteria. This study introduces a methodological tool for supporting decisions on implementing pedestrianization schemes in urban networks. A bi-level network design model variant is developed for that purpose, whose design objective is to maximize the extent of pedestrian streets in an urban network, while maintaining acceptable impacts to the performance of the road network. Alternative decisions on pedestrianization are considered for each network segment; these include partial (one-directional) or complete (bi-directional) pedestrianization under physical and operational criteria and constraints. The model is applied for a mid-sized urban network in Greece and solved using a genetic algorithm. Results show that the pedestrianization of almost 7% of the road network in relation to length leads to a 40% increase in total network travel time, while a higher ratio of complete versus partial pedestrianization is more advantageous. Outcomes also reveal that that rigid design guidelines should be examined in a case-by-case approach, as superior results may be attained if some constraints, such as those related to the overall street width, are relaxed. Reasonably, policy priorities significantly impact generated solutions and are expected to play a decisive role in the design of pedestrianization schemes.

Health & Hope Oasis Air Pollution the Silent Killer

Outdoor air pollution is a leading cause of early death, chronic disease, and disability. Motor vehicles are a major contributor to outdoor air pollution, exposing vulnerable population to especially high levels of harmful emissions in urban areas and near major roadways. Fine particles or particulate matter with a diameter of 2.5 micrometers or less (PM 2.5), are among the most harmful vehicle pollutants and are associated with a range of health impacts including cardiovascular and respiratory diseases, lung cancer, and infant mortality. Using gasoline and diesel as transport fuel causes emissions of both greenhouse gases, driving climate change, and of other air pollutants that are dangerous to human health and the natural environment. To prevent destructive climate change and reduce health impacts, the sector needs to move towards fuel that a lower carbon footprint and reduced emissions of sulfur and other conventional pollutants. Low Carbon Technology called Hydrogen on demand or water as fuel (HHO) Technology successfully reduces emission up to 50% and increase fuel efficiency up to 40%. It work equally well with Gasoline and Diesel fuel and recurring cost is almost zero.

Evaluating Advancements in Bluetooth Technology for Travel Time and Segment Speed Studies

Application of Bluetooth technology has become ubiquitous in the current age of transportation research. The purpose of this article is to explore advancements of Bluetooth technology, applied to transportation studies, using performance metrics such as match rate, travel time, and segment speed through analyzing collected data from several Bluetooth devices. Specifically, the study explores the performance of two advanced Bluetooth devices coupled with classic Bluetooth technology: the demodulator (BT DM), and the low-energy Bluetooth signal additional component (BLE). Data were collected in two locations/phases: (1) along a 0.59-mi segment of interstate and (2) along a 0.52-mi segment of an urban arterial road in Baton Rouge, LA. The data collected were compared with benchmark data sets, gathered during the same period, using manual counts from video footage, radar data, and floating car data (FCD). Comparative analysis showed that BLE produced significantly higher matched rates than BT DM. Furthermore, BLE was able to maintain higher accuracy with increased levels of detection. Results of a Kruskal–Wallis test showed BT DM to have a statistically significant difference with FCD during only one out of three peak periods along the interstate segment. However, BLE matched closest with FCD along the interstate, but shared a significant difference with the benchmark data set during two peak periods along the urban arterial roadway. Considering the level of detection with the accuracy of travel times and segment speeds when compared with the benchmark data, it was evident that the BLE performed better than the BT DM.

Comparison of contributing factors for injury severity of large truck drivers in run-off-road crashes on rural and urban roadways: Accounting for unobserved heterogeneity

Abstract In spite of numerous efforts to quantitatively identify the factors contributing to the injury severity of different crash types in rural and urban settings, the distinction between rural and urban areas regarding the injury severity of run-off-road (ROR) crashes involving large trucks is still not clearly understood. As such, the objective of this study is to investigate the effect of area type (i.e., urban vs. rural) on injury severity outcomes sustained by drivers in ROR crashes involving large trucks while accounting for unobserved heterogeneity. To do this, the latent class ordered probit models with two classes are developed. The crash data pertaining to ROR crashes involving large trucks in Oregon between 2007 and 2014 were utilized. The estimation results reveal that the developed latent class ordered probit models (for urban and rural areas) are substantially distinct in terms of the contributing factors affect urban and rural ROR crash severities. The results indicate that female drivers and speed limit of 55 mph were associated with moderate injuries (non-incapacitating) in rural roadway ROR crashes while no injury outcome is most likely for crashes occurred in urban roadways with raised medians and on areas with a population density between 10,001 and 25,000. Also, the findings show that some factors increase the risk propensity of sustaining higher injury levels regardless of the land use setting such as crashes on horizontal curves, not wearing seatbelt, and driver fatigue. The findings of this study could benefit trucking industry, transportation agencies, and safety practitioners to prevent or alleviate the injury severity of ROR crashes involving large trucks by developing appropriate and cost-effective countermeasures.

Extremely serious crashes on urban roadway networks: Patterns and trends

Extremely serious traffic crashes, defined as having a death toll of two and greater than two, have become a serious safety concern on urban roadways in Louisiana. This study examined the different contributing factors of these crashes to determine significant trends and patterns. We collected traffic crash data from Louisiana during the period of 2013 to 2017 and found that a total of 72 extremely serious crashes (around 2% of all traffic fatalities) occurred on Louisiana urban roadway networks. As crash data contain an enormous list of contributing factors, there was an issue of ‘more features than data points’ in solving the research problem. Most of these variables are categorial in nature. We selected a dimension reduction tool called Taxicab Correspondence Analysis (TCA) to investigate the complex interaction between multiple factors under a two-dimensional map. Findings of the study reveal several key clusters of attributes that show patterns of association between different crash attributes. The conclusions of this study are exploratory, and the results can help in better visualizing the association between key attributes of crashes. The findings have potentials in designing suitable countermeasures to reduce extremely serious crashes.

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

Investigating Los Angeles' urban roadway network from a biologically-formed perspective.

The evolution of networks is constrained by spatial properties of the environment; a characterization that is true in both biological and built networks. Hence built networks such as urban streets can be compared to biological networks to reveal differences in efficiency and complexity. This study assessed foraging networks created by the slime-mold Physarium polycephalum on proportional 3D-printed topographic maps of metropolitan city of Los Angeles, California. Rapidly-generated isomorphic solutions were found to be consistently and statistically shorter than existing roadways in system length. Slime mold also allocated resources to supporting key nodes, analogous to how heavy traffic flows through major intersections. Further, chemical deterrents inhibited exploration of slime mold in selected areas and allows for testing of network redundancy and system resilience, such as after an earthquake or wildfire.

Developing Capacity Reduction Factors for Curbside Bus Stops Under Heterogeneous Traffic Conditions

Traffic characteristics of a roadway are significantly influenced by bus stops, and it becomes more chaotic when a separate bus bay is not provided and the bus has to be stopped in the curb lane. The present study analyzes the effect of such bus stops on the midblock capacity of urban arterials under heterogeneous traffic conditions. Field data have been collected at six sections of six-lane divided urban arterial roads; out of that, three sections have not marked with bus stop and referred to as base sections, whereas the other three have designated bus stops and denoted as friction sections. A set of simultaneous equations are established using speed and flow data to estimate the midblock capacity of urban roads and found to be 6000 PCU/h for one direction of flow. Further, a set of simultaneous equations are developed between the speed of a vehicle type and the densities of all categories of vehicles with densities of stopped buses in their individual terms. These equations are solved for any number of stopping buses at a fixed proportion of vehicles in the traffic stream for different traffic volume levels and thus get the capacity curves. It has been found that the capacity of urban arterials is reduced by 6.6–28.3% when the frequency of stopping buses is as low as 20 bus/h to as high as 120 bus/h, respectively. The study outcomes are useful for designing bus stops and at the same time estimating the capacity of such sections in order to reduce the congestion on the urban roadway network.

Modelling Pedestrian Perspectives in Evaluating Satisfaction Levels of Urban Roadway Walking Facilities

Pedestrian Level of Service (PLOS) is a multifaceted expression which represents the working condition of walking facilities and satisfaction level of pedestrian experience while using these facilities. In this regard this study aimed at developing PLOS models for three pedestrian facilities such as sidewalk, signalized intersection and un-signalized intersection by using qualitative data sets. Total 2730 pedestrian’s real time sense of satisfaction data were collected from ten cities of India. Behavioral responses were taken in order to assess variation with different age and gender from study areas having miscellaneous activities. Taking sets of questions, different variable scores were estimated to provide a wide variation of independent variable that will be more reliable for model fitting. Before model development degree of dependency has been examined with the help of discriminant factor analysis. Implementing ridge regression technique three different PLOS models have been developed for three pedestrian facilities. PLOS scores found by using the proposed models were classified by applying GA-Fuzzy clustering technique in order to get six PLOS ranges (A-F). At signalized intersection 3% of participants were extremely dissatisfied with the facility and they gave rating of >5 for PLOS score. This high score was because of pedestrian speed was severely restricted at some places due to the left and permissible right turning vehicles. Results also revealed that for un-signalized intersection nearly 23% of participants were having PLOS score of >4 as waiting time was more than expected and also pedestrian had to face conflict with vehicles while crossing.

Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map

Automated pavement distress detection benefits road maintenance and operation by providing the condition and location of various distress rapidly. Existing work generally relies on manual labor or specific algorithms trained by dedicated datasets, which hinders the efficiency and applicable scenarios of methods. Street view map provides interactive panoramas of a large scale of urban roadway network, and is updated in a recurrent manner by the provider. This paper proposed a deep learning method based on a pre-trained neural network architecture to identify and locate different distress in real-time. About 20,000 street view images were collected and labeled as the training dataset using the Baidu e-map. Eight types of distress are notated using Yolov3 deep learning architecture. The scale-invariant feature transform (SIFT) descriptors combined with GPS and bounding boxes were applied to judge the deterioration of the distress. A decision tree was designed to evaluate the change of the distress over some time. A typical road in Shanghai was selected to verify the effectiveness of the proposed model. The images of the road from 2015 to 2017 were collected from the street view map. The results showed that the mean average precision of the proposed algorithm is 88.37%, demonstrating the vast potential of applying this method to detect pavement distress. 43 distress were newly generated, and 49 previous distress were patched in the two years. The proposed method can assist the authorities to schedule the maintenance activities more effectively.

Transport Management Characteristics of Urban Hazardous Material Handling Business Entities

In order to minimize the occurrence of large-scale accidents resulting from the transportation of hazardous materials (HAZMAT) on urban roadways, a system to monitor freight vehicle movements in real-time is being implemented. Although monitoring systems are in place, no prioritization strategy has been prepared for the selection of vehicles by the companies handling HAZMAT. Therefore, this study aims to analyze the factors affecting the level of transport management of HAZMAT handling business entities such as the freight, shipping, and vehicle characteristics, and suggests directions for effective policy enforcement of HAZMAT vehicle monitoring strategies. In this study, nation-wide survey data on the logistics status of HAZMAT handling business entities were collected, and the influence of such business entities according to their level of transport management was derived using an ordered logit model. Implications were obtained through statistical analysis of the transport management behavior of urban hazardous material handling business entities. In the future, it is necessary to study empirical methods for setting priorities based on the survey data of the entire population of HAZMAT transport vehicles.

BaroSense

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

Nighttime visual recognition performance of light emitting diode traffic signs

Light emitting diode (LED) traffic signs have been deployed on many urban roadways recently. However, a lack of uniform engineering criteria for the luminance level of LED traffic signs in China brought up consequences such as inaccurate recognition of signs for drivers as well as potential traffic accidents. This paper explores the possible luminance range of LED traffic signs through full-scale field static experiments. Visual recognition distance, character height, character stroke, sign background color, sign dimension, and sign complexity degree are taken into account. The visual recognition luminance value required for drivers are recorded. Experiment results reveal that: for character symbol, the background color of the board and character strokes have no significant effect on driver’s visual recognition performance; for graphic symbol, the higher of graphic complex degree, the higher luminance value required for driver to recognize. This study summarizes the luminance thresholds of different kinds of LED signs for different roadway classes. The findings of this study would provide reference for future design of LED traffic signs.

Temporal and Spatial Fluctuation of Noise Levels in the Closed Vicinity of Urban Roadways

Noise pollution is a very critical issue for a better quality of life in urban settings. This study has been conducted to investigate the temporal and spatial fluctuation of noise levels in the vicinity of urban roadways. A total of twelve sites have been covered, eight in residential area and four in commercial area for this study. The monitoring of noise levels in residential and commercial areas in the capital city of India, Delhi, has been carried out from 18th of July to 12th of August 2017 using Sound Level Meter (Larson & Davis 831). The monitoring has been done only on working days and in good weather condition as per standard procedure, special care has been taken to reduce the effects of wind. Temporal distribution shows that the noise level in morning at the residential area has been more than the prescribed standard and reached up to 84.8 dB (A) in comparison to the commercial areas which has been remains up to 79.46 dB (A). Spatial distribution revealed that the noise level at Ashram Chowk remains in the range of 75 to 80 dB (A) which is maximum in comparison to all other selected location of residential area followed by Moolchand which remains in the range of 70 to 75 dB (A). In commercial area the noise levels remain in the range of 65 to 70 dB (A) at Connaught Place outer Circle (CPOC), which is remain maximum among all selected location. The noise level exceeds the recommendation of CPCB at all eight locations of residential area and two locations of commercial area out of four. It can be concluded that the residential area near urban roadways remains more prone to noise pollution in comparison to commercial area. Planning and public knowledge about the long term noise risk may help in to relieve the noise risk in urban areas.

Public transit and urban poetics: Singapore’s Moving Words poetry project and anthology

ABSTRACT This essay examines the 2011 Moving Words public poetry campaign in Singapore where poems were displayed in many train stations of the Mass Rapid Transit rail system. It also discusses the poetry anthology, Moving Words, containing poems submitted by members of the public as part of the campaign. While existing studies of Singaporean literature and culture discuss urban space, architecture, and roadways as significant literary tropes, no study of the Mass Rapid Transit’s role and representation in poetry exists. In contrast to the Singaporean state’s promotion of neoliberal work-driven time-discipline and lifestyles, the Moving Words poems imagine an affective and temporal sphere of social relations that is more capacious and relational. Drawing on Walter Benjamin’s idea of distraction and socio-cultural studies of urban life and public transit, I analyse the poems by showing how their form and content appropriate the mechanical and regulated spaces of train stations and carriages to transcend civil inattention and explore alternative temporal rhythms and emotional states. Disrupting the calibrated coercion of Singaporeans as docile passengers and efficient workers, the poems encourage readers to imagine relationships with each other and with the country’s past and future that exceed neoliberalism’s relentless focus on the present and productivity.

Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements

Urban forests are often heavily populated by street trees along right-of-ways (ROW), and monitoring efforts can enhance municipal tree management. Terrestrial photogrammetric techniques have been used to measure tree biometry, but have typically used images from various angles around individual trees or forest plots to capture the entire stem while also utilizing local coordinate systems (i.e., non-georeferenced data). We proposed the mobile collection of georeferenced imagery along 100 m sections of urban roadway to create photogrammetric point cloud datasets suitable for measuring stem diameters and attaining positional x and y coordinates of street trees. In a comparison between stationary and mobile photogrammetry, diameter measurements of urban street trees (N = 88) showed a slightly lower error (RMSE = 8.02%) relative to non-mobile stem measurements (RMSE = 10.37%). Tree Y-coordinates throughout urban sites for mobile photogrammetric data showed a lower standard deviation of 1.70 m relative to 2.38 m for a handheld GPS, which was similar for X-coordinates where photogrammetry and handheld GPS coordinates showed standard deviations of 1.59 m and the handheld GPS 2.36 m, respectively—suggesting higher precision for the mobile photogrammetric models. The mobile photogrammetric system used in this study to create georeferenced models for measuring stem diameters and mapping tree positions can also be potentially expanded for more wide-scale applications related to tree inventory and monitoring of roadside infrastructure.

Travel Time Reliability Analysis on Selected Bus Route of Mysore Using GPS Data

This research study aims at evaluating the travel time reliability indices like Buffer Time Index, Planning Time Index and Travel Time Index over space and time on selected city bus route of Mysore city, India. Also, this study focuses on developing the correlation models between the bus journey speed and flow for different type of urban roadway conditions. These models are observed to be performing better and hence can be used for predicting traffic volumes for similar roadway conditions when traffic data are not available from field. Further, the variation of volume-to-capacity (V/C) with travel time reliability indices is also studied in detail. The k-means clustering analysis is performed for classifying the reliability measures with respect to V/C and Coefficient of Variation. It is expected that the developed reliability measures may be useful for defining level of service for bus routes in class-II cities (cities with lesser population than metropolitan cities).

CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain)

Abstract. The NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona.