Middle-class Household Consumption in Dhaka, Bangladesh: Understanding Current Practices Related to Food, Electricity, Transport and Attire

Integrating computer vision and traffic modeling for near-real-time signal timing optimization of multiple intersections

Urban green space use is often associated with improved physical and mental health and lower noncommunicable disease (NCDs) burdens. Factors that influence green space visits have been documented in cities of the Global North, but evidence of urban green space use patterns for cities in the Global South is scarce. The aim of this study is to investigate factors influencing urban green space use patterns in Dhaka, Bangladesh, a megacity of the Global South, with a particular focus on how poor health condition and healthcare professionals’ prescriptions to exercise outdoors (park prescriptions—ParkRx) impact the green space use of middle-aged adults. We collected green space characteristics and use factors (i.e., availability, accessibility, attractiveness, and attachment), health condition, ParkRx, and urban green space use intensity (i.e., frequency and duration) via a self-reported questionnaire from 169 middle-aged residents of Dhaka. We used multivariate modeling to estimate the association of green space characteristics, health condition, and ParkRx with use intensity. We further applied a mediation analysis to determine the influence of ParkRx on the relationship between residents’ poor health conditions and use intensity. We found that green space availability and accessibility did not significantly influence use intensity, but attractiveness was negatively associated with use intensity. Green space use intensity was significantly and positively associated with attachment to the green space, poor health condition (i.e., having noncommunicable diseases), and ParkRx. ParkRx significantly mediated the relationship between health condition and use intensity. We observed limited supply, poor access, and low attractiveness when studying the urban green spaces in Dhaka, but these qualities did not affect use intensity, as found in many case studies in the Global North. In contrast, urban green space use intensity in our case study is mostly dependent on poor health condition and park prescriptions.

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Noncommunicable Diseases, Park Prescriptions, and Urban Green Space Use Patterns in a Global South Context: The Case of Dhaka, Bangladesh

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections.

Signal Timing Optimization Model Based on Bus Priority

Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map’s traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.

Modeling Traffic Congestion in Developing Countries Using Google Maps Data

A growing body of research has applied intelligent transportation technologies to reduce traffic congestion at signalized intersections. However, most of these studies have not considered the systematic integration of traffic data collection methods when simulating optimum signal timing. The present study developed a three-part system to create optimized variable signal timing profiles for a congested intersection in Dhaka, regulated by fixed-time traffic signals. Video footage of traffic from the studied intersection was analyzed using a computer vision tool that extracted traffic flow data. The data underwent a further data-mining process, resulting in greater than 90% data accuracy. The final data set was then analyzed by a local traffic expert. Two hybrid scenarios based on the data and the expert’s input were created and simulated at the micro level. The resultant, custom, variable timing profiles for the traffic signals yielded a 40% reduction in vehicle queue length, increases in average travel speed, and a significant overall reduction in traffic congestion.

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Shariful Hasnine Sabuj

Papers

Integrating Data Mining and Microsimulation Modelling to Reduce Traffic Congestion: A Case Study of Signalized Intersections in Dhaka, Bangladesh