Perceived health effects of traffic congestion among commuters in Ota city, Nigeria

Cities in the developing world have not been spared from the congestion problems due to economic growth and subsequent increase in vehicle ownership by both private and public owners. Traffic congestion is a major challenge in Gaborone, especially in the Western by pass road of the city resulting in massive delays, a decrease in productivity, an increase in environmental pollution, and an increase in travel time. The objective of this paper is to analyse urban transportation congestion, identify causes and proffer solutions through engaging road user perceptions, transport planning authorities and transport policy makers in Botswana. The study employed an exploratory design to sample 208 respondents through the administration of questionnaires. Quantitative data were used and analysed using SPSS v 21. The study revealed that there is massive influx of motor vehicles in the city, inadequate urban infrastructure, poor infrastructural planning, poor or no urban transport policy framework, and Road Traffic Incidents were the main causes of traffic congestion. The effects of traffic congestion are higher transport costs, massive delays, pollution, decreased productivity and stress to motorists as a result of longer travelling time. The study recommends improvement of road infrastructure especially intersections with traffic circles, implementation of mass transit system in transporting public within the city, public education on effects of congestion, implementation of smart urban transportation, mobility technologies (transport intelligence systems) and implementation of non-motorised mobility. Keywords : congestion, Gaborone, urban transport, Botswana, mass transit, Main Mall, central business district. DOI: 10.7176/JESD/11-8-06 Publication date: April 30 th 2020

The effects of traffic congestion on travel behavior are complex and multidimensional because they are related to various factors such as density, land use patterns, network connectivity, and individual preferences. Traffic congestion is a phenomenon that not only affects transportation systems but also influences commuters’ quality of life and population mobility. The present research aims to analyze the effects of traffic congestion on individuals’ travel behaviors, addressing both direct and indirect effects of congestion on vehicle miles traveled (VMT) per driver by implementing structural equation modeling (SEM) techniques. In addition to the causal analysis between traffic congestion and VMT, this study examined the complex relationship between an individual’s socioeconomic characteristics, the built environment, congestion, and VMT. Measuring local congestion at a national level is also a key contribution of this research. This study used the same methodology as the Texas A&M Transportation Institute to compute a road congestion index and quantify local congestion for 93,769 drivers within 337 metropolitan areas. Our findings suggest that congestion is the main driver of VMT reduction. The findings also confirm that residents in compact development regions have lower daily VMTs because of the proximity of origins and destinations in denser areas with higher job–population balances. Therefore, rather than expanding highway networks, public transit investment might address traffic congestion more efficiently—not only by providing residents with more equitable and sustainable means of transportation, but also by encouraging people to reside in more compact and location-efficient areas.

In this study, we aim to solve the shared autonomous vehicle (SAV) routing problem under the effects of congestion in the road network. The SAV routing problem is the problem of finding an optimal SAV-traveler assignment as well as the SAV route choice. Since widespread use of SAVs would cause significant congestion of road networks, it is essential to consider the effects of traffic congestion on SAV route choice. We develop a tabu search (TS) heuristic to solve for SAV routing problem. The heuristic aims to minimize the total person travel time experienced by travelers by exploring the solution space using a swap procedure. The total person travel time is defined as the total time spent by all travelers entering the network in an SAV trip. A Nearest Traveler Neighborhood is defined to choose candidate travelers to consider for the swap procedure. An agent based simulation of the traffic network is used to determine the experienced travel times for each solution from the TS heuristic. The Sioux Falls network is used to test the performance of TS for various demand and fleet sizes. A series of experiments are performed to understand the sensitivity of the heuristic to its parameters and the congestion in the road network. The heuristic is found to produce encouraging results in reducing the total person travel time for differing fleet sizes and demand levels.

Undoubtedly traffic congestion continues to be a challenge that negatively impacts socio-economic activities in most cities in the world. It does not only hinder the smooth movement of people but also freight and services. Admittedly, there have been a number of studies that have examined the causes and effects of traffic congestion in Ghanaian cities; nonetheless, these studies have largely concentrated on the larger metropolis which includes Accra and Kumasi with little attention given to mid-sized emerging urban centers. Using data collected from field survey, in-depth interviews and field observations in Sekondi-Takoradi, this study examines the effects of traffic congestion on socio-economic activities along five major road corridors in the study area. The survey data were analyzed using descriptive and inferential statistics while in-depth interviews were integrated with the results using quotes from informants. The findings from the study revealed a spatial variation of the effects of traffic congestion along the five corridors studied. In particular, it was observed that increased traffic congestion led to the reduction in sales made by traders and commercial drivers, while students and workers who used these roads also asserted they often get to school and workplaces very late. The study therefore recommends road expansion along these routes and use of road tolls to regulate the activities of ‘trotro’ (public buses) and taxis in the city in order to minimize traffic congestion in the city. Key words: Traffic congestion, socio-economic activities, spatial variation, corridors, Takorad, Metropolis.

Drawing on survey data for the year 2023 of urban Pakistan, we aim to explain the complex mechanism by which traffic congestion affects subjective wellbeing (SWB). The sample comprises federal and provincial capital cities and is allocated proportionally. We use the Yamane formula to determine the sample size of 400 respondents. The data is collected through stratified random sampling. We add to the happiness studies by applying a structural equation modelling framework to analyse the effects of traffic congestion on satisfaction with environmental quality; satisfaction with commuting time and subjective wellbeing in general. Using commute-time duration, noise pollution and air quality index as instrumental variables for traffic congestion index, we find that traffic congestion index is negatively associated with SWB, satisfaction with commuting time and satisfaction with environmental quality. In contrast, the self-reported health status has a positive influence and is the major determinant of SWB that fully mediates the relationship between traffic congestion and SWB. Moreover, public transportation adds to wellbeing and combats the negative effects of traffic congestion. To improve general wellbeing and to reduce traffic congestion, the government is advised to enhance investment in public transportation, discourage the excessive use of private vehicles, and encourage public transport and non-motorized vehicles.

Novel Use of Integrated Map Software and Traffic Congestion Data Affecting Staff Travel Time to PCI for STEMI

Particulate matter negatively affects personal health and daily living; hence, it has become a popular topic in academia. Transportation-related variables have thus far not been extensively explored, even though various factors associated with particulate matter have been investigated. In particular, the amount of particulate matter emitted from vehicles depends on the speed of travel, which can be adversely affected by traffic congestion. Hence, we aim to examine the causal relationships between concentrations of particulate as well as ultrafine particulate matter and traffic congestion. To investigate the effects of traffic congestion on concentration of particulate matter, we used big data regarding various urban parameters, such as travel speed, air pollution, and climatological data, by employing fixed-effects panel data analysis models. We use the real-time congestion index (RCI) to calculate traffic congestion, in addition to precipitation, wind speed, wind direction, and temperature as the control variables. The main findings from the panel models are as follows: First, traffic congestion is directly related to the concentrations of particulate and ultrafine particulate matter. In particular, an increase in one unit of RCI increases the concentration of the particulate matter by 3.3 ㎍/㎥ and ultrafine particulate matter by 1.7 ㎍/㎥, after accounting for other factors. Second, the impact of traffic congestion is greater when the concentration of particulate matter is less than 30 ㎍/㎜ (also called the “good condition“ of PM10) and that of ultrafine particulate matter is above 35 ㎍/㎜. (called the “bad condition” of PM2.5). Third, the effects of traffic congestion on particulate matter are greater during non-rush hours than during rush hours. This is probably because of the greater number of vehicles on the roads during non-rush hours, such as older/obsolete vehicles, trucks, and diesel-powered vehicles, which are likely to emit more pollutants. Our results suggest that alleviating traffic congestion in addition to existing air pollution policies could improve air quality, even though such effects may not be substantial.

ABSTRACTAlthough there is an abundant regional literature analyzing traffic congestion, only a few studies have explored extending such analysis with spatial effects. This study uses a dynamic spatial Durbin model and city-level panel data for the period 2003–14 to investigate the spatial spillover effects of traffic congestion on urbanization in China. The results show that there is an inverted ‘U’-shaped relationship between urbanization and traffic density in local and neighbouring cities, and congestion effects have appeared. In the short and long run, the spatial effects of traffic congestion have become an important force restricting the effective promotion of urbanization in China.

Externalities of auto traffic congestion growth: Evidence from the residential property values in the US Great Lakes megaregion

Stuck in traffic: Do auditors price traffic congestion?

Road congestion and air pollution are key challenges for quality of life in urban settings. This research leverages highly disaggregated crowdsourced data from Latin America to study the effect of road congestion on levels of carbon monoxide, nitrogen dioxide, and particulate matter in four of the most congested cities in developing countries: Bogota, Buenos Aires, Mexico City, and Santiago. Based on a panel data econometric approach with over 4.4 billion records from Waze and hourly data from 54 air monitoring stations for 2019, our two-stage least square model shows a cumulative increase of 0.6% in response to a 1% of road congestion on the three air pollutants. Moreover, we find a nonlinear relationship between road congestion and air quality and estimate the threshold above which the effect decays. This study provides evidence that supports public policies designed to make urban mobility more sustainable by implementing measures to reduce road congestion in developing contexts.

Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city’s residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers’ preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers’ willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population’s geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers’ sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion.

This study mainly addresses two main questions: (1) whether traffic congestion negatively affects single-family house price by constraining accessibility to jobs; (2) whether congestion effects and accessibility effects vary by income groups within a metropolitan area. This study uses a multilevel hedonic price model to estimate the marginal price of accessibility while controlling for other neighbourhood attributes and the correlation of proximal housing sales. The congestion effects are identified by comparing the implicit price of accessibility between congested-flow and free-flow. The results show that the accessibility measured with congested time yields higher marginal price, suggesting that households are willing to pay more to avoid locations with high congestion delays and accessibility loss. The results also suggest that accessibility effects are more valued by homebuyers in middle-income neighbourhoods, compared with those in the lowest or highest income neighbourhoods.

A novel transit accessibility measure is proposed following a utility framework to account for the stochasticity in transit services, the effect of real-time information, traffic congestion, and structural features of the transit network, which are disregarded in prior literature. The quantification of accessibility is formulated as a network optimization problem. Through a case study using real-world transit data, the impact of the above features on accessibility was investigated. The results indicate that accessibility is a random quantity exhibiting considerable within-day, day-to-day, and spatial variation across stops, and the disutility from travel time components should be weighted differently for an unbiased estimate. Traffic congestion, represented by the average pace on alternate paths, decreases accessibility linearly. The transit network structure was found to strongly affect accessibility, where the number of available alternative paths had a positive cubic effect, and the degree of overlap among paths had a negative influence on the percentage of improvement in accessibility. Moreover, besides making supply changes, accessibility can be enhanced by providing accurate real-time information on bus arrivals, and the benefit increases quadratically as a function of the out-of-vehicle travel time reduction. The proposed measure, visualization, and analysis methods used, and recommendations made will help transit agencies and planners identify critical locations for interventions to improve transit service accessibility across space and time.

Estimating the effect of road congestion on air quality in Latin America