scholarly journals CYCLE LANES: THEIR EFFECT ON DRIVER PASSING DISTANCES IN URBAN AREAS

Transport ◽  
2014 ◽  
Vol 29 (3) ◽  
pp. 307-316 ◽  
Author(s):  
Kathryn Stewart ◽  
Adrian McHale
Keyword(s):  
Current Literature ◽  
Urban Areas ◽  
Time Of Day ◽  
Unknown Variable ◽  
Unknown Factor ◽  
Road Width ◽  
Contradictory Evidence

The current literature in the field of cycle lanes has often shown contradictory evidence as to the benefits and risks of cycle lanes and previous work has specifically shown that on higher speed roads, drivers may pass closer to a cyclist when a cycle lane is present. Utilising an instrumented bicycle, we collected information as to the passing distance demonstrated by drivers when overtaking a cyclist within the urban (30 mph/40 mph) environment. The presented analysis shows that when a driver encounters a cyclist mid-block (i.e. not at a junction), there are more significant variables than the presence of a cycle lane that determines the overtaking distance. The three most significant variables identified are: absolute road width, the presence of nearside parking and the presence of an opposing vehicle at the time of an overtaking manoeuvre. The analysis also demonstrated that there is a larger unknown factor when it comes to overtaking distances. We postulate that this unknown variable is the driver himself and will vary by area, site and even time of day (i.e. different driving cultures, congestion, or frustration during peak times, etc.) making it difficult to quantify.

scholarly journals Evaluating human–coyote encounters in an urban landscape using citizen science

2020 ◽  
Author(s):  
David Drake ◽  
Shelli Dubay ◽  
Maximilian L Allen
Keyword(s):  
Citizen Science ◽  
Urban Areas ◽  
Urban Landscape ◽  
Time Of Day ◽  
Human Observer ◽  
Public Outreach ◽  
The Public ◽  
Paved Road ◽  
Almost All ◽  
Human Wildlife Conflict

Abstract Coyotes are ubiquitous in habitats across North America, including in urban areas. Reviews of human–coyote encounters are limited in scope and analysis and predominantly document encounters that tend to be negative, such as human–wildlife conflict, rather than benign experiences. The objective of our study was to use citizen science reports of human–coyote interactions entered into iNaturalist to better understand the range of first person accounts of human–coyote encounters in Madison, WI. We report 398 citizen science accounts of human–coyote encounters in the Madison area between October 2015 and March 2018. Most human–coyote encounters occurred during coyote breeding season and half of all encounters occurred in moderate development land cover. Estimated level of coyote aggressiveness varied significantly, with 90% of citizen scientists scoring estimated coyote aggression as a 0 and 7% scoring estimated aggression as a 1 on a 0–5 scale (with 0 being calm and 5 being aggressive). Our best performing model explaining the estimated distance between the human observer and a coyote (our proxy for a human–coyote encounter) included the variables distance to nearest paved road, biological season of the year relative to coyote life history, and time of day/night. We demonstrate that human–coyote interactions are regularly more benign than negative, with almost all first-hand reported human–coyote encounters being benign. We encourage public outreach focusing on practices that can foster benign encounters when educating the public to facilitate human–coyote coexistence.

Improved Modeling of Non-Home-Based Trips

1996 ◽  
Vol 1556 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Gordon W. Schultz ◽  
William G. Allen
Keyword(s):  
Detailed Analysis ◽  
Urban Areas ◽  
Mode Choice ◽  
Time Of Day ◽  
Place Of Residence ◽  
Trip Length ◽  
Trip Purpose ◽  
Trip Chaining ◽  
Home Based ◽  
A Chain

Non-home-based (NHB) trip making typically accounts for about 25 to 30 percent of travel by individuals in urban areas. However, the NHB trip purpose is usually treated as a large unknown category, and little attention is paid to the nature of these trips. An effort to better understand the characteristics of NHB trips by subdividing the NHB trip category is described. It is hoped that this effort will serve as a useful precursor to improving the analysis of trip chaining behavior. By definition, NHB trips are almost always part of a chain of trips that usually starts or ends at the trip maker's place of residence or work. By examining this chain more closely, it is possible to group NHB trips into two or three categories. More detailed analysis of these categories reveals that they have very different trip length, mode choice, and time of day characteristics. Making this subdivision improves the accuracy of the model, increases the sensitivity of the forecast to important factors, and provides a greater understanding of trip chaining behavior.

scholarly journals Determination of tropospheric vertical columns of NO<sub>2</sub> and aerosol optical properties in a rural setting using MAX-DOAS

2011 ◽  
Vol 11 (23) ◽  
pp. 12475-12498 ◽  
Author(s):  
J. D. Halla ◽  
T. Wagner ◽  
S. Beirle ◽  
J. R. Brook ◽  
K. L. Hayden ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Radiative Transfer ◽  
Urban Areas ◽  
Source Region ◽  
Time Of Day ◽  
Rural Setting ◽  
Radiative Transfer Model ◽  
Optical Absorption Spectroscopy ◽  
Transfer Model ◽  
Vertical Column

Abstract. Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements were performed in a rural location of southwestern Ontario during the Border Air Quality and Meteorology Study. Slant column densities (SCDs) of NO2 and O4 were determined using the standard DOAS technique. Using a radiative transfer model and the O4 SCDs, aerosol optical depths were determined for clear sky conditions and compared to OMI, MODIS, AERONET, and local PM2.5 measurements. This aerosol information was input to a radiative transfer model to calculate NO2 air mass factors, which were fit to the measured NO2 SCDs to determine tropospheric vertical column densities (VCDs) of NO2. The method of determining NO2 VCDs in this way was validated for the first time by comparison to composite VCDs derived from aircraft and ground-based measurements of NO2. The new VCDs were compared to VCDs of NO2 determined via retrievals from the satellite instruments SCIAMACHY and OMI, for overlapping time periods. The satellite-derived VCDs were higher, with a mean bias of +0.5–0.9×1015 molec cm−2. This last finding is different from previous studies whereby MAX-DOAS geometric VCDs were higher than satellite determinations, albeit for urban areas with higher VCDs. An effective boundary layer height, BLHeff, is defined as the ratio of the tropospheric VCD and the ground level concentration of NO2. Variations of BLHeff can be linked to time of day, source region, stability of the atmosphere, and the presence or absence of elevated NOx sources. In particular, a case study is shown where a high VCD and BLHeff were observed when an elevated industrial plume of NOx and SO2 was fumigated to the surface as a lake breeze impacted the measurement site. High BLHeff values (~1.9 km) were observed during a regional smog event when high winds from the SW and high convection promoted mixing throughout the boundary layer. During this event, the regional line flux of NO2 through the region was estimated to be greater than 112 kg NO2 km−1 h−1.

scholarly journals Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

2021 ◽  
Vol 6 (1) ◽  
pp. 35
Author(s):  
Yazan Qarout ◽  
Yordan P. Raykov ◽  
Max A. Little
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Smart Cities ◽  
Low Cost ◽  
Deep Understanding ◽  
Time Of Day ◽  
Sensor Data ◽  
Observation Data ◽  
Transition Matrices ◽  
Time Dynamics

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport, and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative which enables deep understanding of population behaviour, such as the Global Positioning System (GPS) data. However, the automated analysis of such low-dimensional sensor data requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day, or the difference between weekend/weekday trends. We propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations, all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM), is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

scholarly journals Particle number emissions of motor traffic derived from street canyon measurements in a Central European city

2009 ◽  
Vol 9 (1) ◽  
pp. 3763-3809 ◽  
Author(s):  
S. Klose ◽  
W. Birmili ◽  
J. Voigtländer ◽  
T. Tuch ◽  
B. Wehner ◽  
...  
Keyword(s):  
Wind Direction ◽  
Urban Areas ◽  
Street Canyon ◽  
Particle Number ◽  
Emission Factors ◽  
Time Of Day ◽  
Traffic Emissions ◽  
Size Spectrum ◽  
Size Distributions ◽  
The Impact

Abstract. A biennial dataset of ambient particle number size distributions (diameter range 4–800 nm) collected in urban air in Leipzig, Germany, was analysed with respect to the influence of traffic emissions. Size distributions were sampled continuously in 2005 and 2006 inside a street canyon trafficked by ca. 10 000 motor vehicles per day, and at a background reference site distant at 1.5 km. Auto-correlation analysis showed that the impact of fresh traffic emissions could be seen most intensely below particle sizes of 60 nm. The traffic-induced concentration increment at roadside was estimated by subtracting the urban background values from the street canyon measurement. To describe the variable dispersion conditions inside the street canyon, micro-meteorological dilution factors were calculated using the Operational Street Pollution Model (OSPM), driven by above-roof wind speed and wind direction observations. The roadside increment concentrations, dilution factor, and real-time traffic counts were used to calculate vehicle emission factors (aerosol source rates) that are representative of the prevailing driving conditions, i.e. stop-and-go traffic including episodes of fluent traffic flow at speeds up to 40 km h−1. The size spectrum of traffic-derived particles was essentially bimodal – with mode diameters around 12 and 100 nm, while statistical analysis suggested that the emitted number concentration varied with time of day, wind direction, particle size and fleet properties. Significantly, the particle number emissions depended on ambient temperature, ranging between 4.8 (±1.8) and 7.8 (±2.9).1014 p. veh−1 km−1 in summer and winter, respectively. A separation of vehicle types according to vehicle length suggested that lorry-like vehicles emit about 80 times more particle number than passenger car-like vehicles. Using nitrogen oxide (NOx) measurements, specific total particle number emissions of 338 p. (pg NOx)−1 were inferred. The calculated traffic emission factors, considering particle number and size, are anticipated to provide useful input for future air quality and particle exposure modelling in densely populated urban areas.

scholarly journals Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 784
Author(s):  
Yazan Qarout ◽  
Yordan P. Raykov ◽  
Max A. Little
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
Smart Cities ◽  
Low Cost ◽  
Deep Understanding ◽  
Time Of Day ◽  
Sensor Data ◽  
Observation Data ◽  
Transition Matrices ◽  
Time Dynamics

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

A time of day analysis of crashes involving large trucks in urban areas

2015 ◽  
Vol 75 ◽  
pp. 155-163 ◽  
Author(s):  
Jasmine Pahukula ◽  
Salvador Hernandez ◽  
Avinash Unnikrishnan
Keyword(s):  
Urban Areas ◽  
Time Of Day

scholarly journals The study of effects of greenery on temperature reduction in urban areas

2018 ◽  
Vol 22 (Suppl. 4) ◽  
pp. 988-1000 ◽  
Author(s):  
Jelena Djekic ◽  
Petar Mitkovic ◽  
Milena Dinic-Brankovic ◽  
Milica Igic ◽  
Petar Djekic ◽  
...  
Keyword(s):  
Urban Space ◽  
Urban Areas ◽  
Central City ◽  
Time Of Day ◽  
Tree Canopy ◽  
Surface Cooling ◽  
Temperature Reduction ◽  
Urban Greenery ◽  
Green Areas ◽  
The Impact

Urban landscape is largely characterized by high degree of built space, high share of artificial surface material and the reduction of green areas, which leads to changes in the microclimate and the deterioration of thermal comfort in out-door urban space. One of the most important roles of urban greenery is the impact on the reduction of air temperature due to less heating of green space com-pared to paved surfaces and due to tree shading. The paper analyses the influence of urban greenery on temperature reduction. Aim of the study was to measure the difference in warming up of grassy surfaces and paving materials commonly used for public areas and to evaluate the impact of tree shading on the surface cooling during the day. For this purpose, measuring of surface temperatures was performed during the summer months in 2015 in the central city zone of the city of Nis. The measuring included: grass, asphalt as most commonly used paving material, and concrete tiles commonly used for pedestrian areas. Results show the temperature of grass is significantly lower than the temperature of paved surface at any time of day. In the case of paved surfaces, temperature of shaded or partially shaded material is lower than the temperature of surface exposed to sunlight during the whole day, a temperature difference exists even after nocturnal cooling. The results indicate the importance of green areas for cooling of urban spaces, due to their lower warming and surface shading from tree canopy.

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