Congestion In Urban Areas Research Articles

Construction schedule, an influential factor on air pollution in urban infrastructure projects

Urbanization growth and aging infrastructures necessitate new infrastructure construction projects in congested urban areas. Urban construction projects interrupt the regular on-road vehicle traffic flow that affects air pollution concentration in the adjacent areas. The project schedule, however, is a possible contributor to the air pollution which has been neglected to date. This research proposes a new framework to account for the impact of different urban project schedule alternatives on air pollution emission near the construction zones. The proposed framework uses the capabilities of vehicle traffic simulation to evaluate air pollution emission of different construction schedule alternatives and reduce the resulting emission in urban construction projects. The capability of the proposed framework is verified in a real grade separation case. Pollution concentration shows a potential reduction of up to 41% in sensitive locations. The achieved resulting values represent potential emission reduction of 7.8% for CO2, 8.2% for NOx, and 3.8% for PM10. The achieved results in the case study confirm the impact of the construction schedule on the air pollution emission throughout the project’s construction. It justifies the application of the framework in the congested urban areas. The proposed framework in the research contributes to the sustainable improvement of urban infrastructure projects.

Settlement behaviors of existing tunnel caused by obliquely under-crossing shield tunneling in close proximity with small intersection angle

As an increasing number of metro tunnels are being constructed in congested urban areas, it is inevitable that new tunnels are being constructed in close proximity to existing tunnels. The settlement behaviors of existing tunnel caused by under-crossing tunneling have been intensively investigated; however, settlement characteristics of an existing tunnel caused by under-crossing tunneling in close proximity with small intersection angle is rarely explored. To fill this gap of knowledge, this paper presents a special case study of a shield tunnel obliquely constructed in close proximity to an above existing tunnel with a small intersection angle. Based on monitoring data and numerical simulations using finite difference method (FDM), the settlement characteristics of the existing tunnel were studied. The field monitoring data indicates that the deformation of existing tunnel caused by shield tunneling underneath was dominated by vertical settlement, accompanied by torsional deformation. The existing tunnel experienced the largest settlement and torsional deformation in the intersection zone. The numerical simulation results confirm that existing tunnel tracks experience vertical settlement and torsional deformation, which are in line with the field monitoring results; the settlement trough of existing tunnel becomes increasingly wide and deep with advancement of the under-crossing tunneling. The position of the maximum settlement moves in the direction of tunnel advancing but ultimately stays still in the intersection zone.

Price versus Quantity Measures to deal with Pollution and Congestion in Urban Areas: A Political Economy Approach

Pollution and congestion in urban areas are serious externalities that can be mitigated through the adoption of either price- or quantity-based mechanisms. While price restrictions are occasionally applied, quantity constraints based on car vintage are becoming increasingly popular. Our model provides a comprehensive analysis that explains this prevalence of quantity over price schemes. We also elucidate some other stylized facts observed in urban areas applying traffic restrictions, such as the implementation of hybrid price-and-quantity systems, the use of trial periods and commitments to invest in public transit to enhance the acceptability of urban tolls, and the concentration of quantity restrictions in high-income cities.

A Comparison of Deep Learning Methods for Urban Traffic Forecasting using Floating Car Data

Cities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue in navigation and route planning applications. Furthermore, the pervasive penetration of information and communication technologies makes floating car data an important source of real-time data for intelligent transportation system applications. This paper deals with the problem of forecasting urban traffic when floating car data is available. A comparison of four deep learning methods is presented to demonstrate the capabilities of the neural network approaches (recurrent and/or convolutional) in solving the traffic forecasting problem in an urban context. Different tests are proposed in order to not only evaluate the developed deep learning models, but also to analyze how the penetration rates of floating cars affect forecasting accuracy. The presented experiments were designed according to a microscopic traffic simulation approach in order to emulate floating car data fleets, which provide vehicle position and speed, and to validate the obtained results. Finally, some conclusions and further research are presented.

Complexity Analysis of Data Aggregation and Routing Algorithms for Automated Utility Management Using WSN

At present most of the houses in the country have the traditional electromechanical or digital utility usage meters, water meter, and gas meters. Presently most of the utility meter reading, billing system and utility management is not automated. The recent advances in the wireless sensor networks (WSNs) have made strong impact on the development of low cost remote monitoring systems. The WSN based remote automated utility management, remote meter reading and billing for future smart cities will enhance the quality and service by government. This increases the revenue of government. Due to unpleasant trend in the growth of congestion in urban areas, the smart utility meter data traffic aggregation and routing faces more challenges in the traditional automated service departments. In this work we propose an integrated architecture that include electricity, water and gas utility meters and discuss the methodology for Data Aggregation and Routing for Integrated Public Utility Services (IPUS-DAR) using WSN network. This work aims to integrate three types of utility meters and minimize redundant routing data in the network by applying data aggregation that improves traffic performance. We discuss the computational complexity of proposed Data Routing with Data Aggregation algorithm. The comparative analysis of proposed Data Routing with Data Aggregation methodology with previous methods is analyzed. We investigate performance metrics which include packet delivery ratio, end-to-end delay, jitter, throughput and energy consumption with respect to varying network size using QUALNET.

Selfish Yet Optimal Routing by Adjusting Perceived Traffic Information of Road Networks

Traffic congestion in urban areas causes economic and time loss. Such traffic congestion is caused by selfish routing where users aim to minimize their own travel time. Even if a navigation system provides them with recommended routes, they may not follow the routes, due to dissatisfaction with the expected travel time. In this article, inspired by the concept of “Nudge,” we propose “selfish yet optimal routing,” where all users rationally aim to minimize their own travel time but social optimal routing emerges from such selfish routing by adjusting their perceived traffic information appropriately. We propose a scheme to derive nudging traffic information that fills the gap between selfish routing criterion and altruistic one by internalizing the marginal cost into the perceived traffic information. Users will conduct the selfish routing under their perceived traffic information, which unconsciously results in the optimal routing. Through numerical experiments using both the artificial road network and the real one, we show that the proposed scheme achieves almost the same performance compared with the optimal routing. In addition, the proposed scheme reduces the average travel time by 19.1% compared with notification of actual traffic information, in case of the central-area road network of Nagoya city, Japan.

Multirotor for Medical Aid Kit Transport

The popularity of the multirotor or unmanned aerial vehicle (UAV) is rapidly growing in the field of aerial robotics. In fact, multirotor has now become a standard platform for robotics research worldwide and it has been increasingly used for various constructive purposes across several sectors. Traffic congestion in urban areas has caused a longer time to deliver medical aid kits to the accident sites. Many patients in remote areas with limited road access will need to travel further and consequently take a longer time to reach healthcare centers for their medical needs. In view of this situation, medical drones have the potential to resolve these problems. However, Malaysia has limited regulations and studies regarding the transportation of medical aid kits using multirotors. This study is conducted to develop a multirotor for deployment of medical aid kits. The proposed multirotor is a hexarotor, equipped with Pixhawk flight controller and payload release mechanism, known as NAMTOR 3. From the preliminary flight tests to assess the performance of NAMTOR 3, it has been found that NAMTOR 3 is stable during flight and ready for deployment.

On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.

Achieving Sustainable Public Transport System Adapting BREEAM Assessment Method, A Case of Dhaka, Bangladesh

Lack of affordable, efficient and safe transportation system has various adverse effects on the city. Inadequate malfunctioning transport, the absence of a mass transit system and paratransit against the demand of such an extensive population result in huge traffic congestion, accidents, climate change, and air pollution. Many developing countries are implementing environmental assessment methods for successful planning in congested urban areas. BREEAM is widely followed as a successful environmental method to achieve environmental sustainability. The aim of this research is to critically evaluate the possibility of implementing BREEAM’s assessment to reduce the current problems and to achieve high-quality sustainability in public transport system in Dhaka.

Integration of sun-tracking shading panels into window system towards maximum energy saving and non-glare daylighting

Vertical facades of buildings bear a great potential of solar exposures, especially for congested urban areas with numerous high-rise buildings. Previous studies on the sun-tracking shading devices strayed into the trade-off between energy consumption and glare protection. Here we first built the integrated models which couple window with sun-tracking shading panels for the southerly oriented window. Secondly, the control methods of one-direction-automatic-rotation (1-DAR) and two-direction-automatic-rotation (2-DAR) with sun-tracking function were mathematically proven to be not able to achieve either the minimum energy consumption or the completely non-glare daylighting, under the reasonable assumptions. Then, we have proposed an optimum rotation control method of perpendicular sun-tracking three-direction-automatic-rotation (3-DAR), and demonstrated that the 3-DAR combined with perpendicular sun-tracking method could realize the afore-mentioned goals. Based on the energy saving and glare protection, the movement trajectories and rotation orders of the shading panels throughout the year were explored. Research results show that the annual electricity use in a study office room located in Hangzhou (China), integrated with shading panels under 3-DAR control, is lower than the alternatives under 2- or 3-DAR control. Furthermore, the proposed 3-DAR incorporated with the perpendicular sun-tracking control method reveals a better protection against the sun-glare, and is also applicable to the horizontal windows (i.e., clerestory), by proposing the rotation orders and deriving the equations of movement trajectories in a similar way.

Real-Time Route Planning and Data Dissemination for Urban Scenarios Using the Internet of Things

Vehicular ad hoc networks (VANETs) have gained interest because of their applicability and significance in the fields of traffic management, road monitoring and safety, infotainment, and on-demand services. Route planning in vehicular networks based on efficient collection of real-time data can effectively mitigate traffic congestion problems in urban areas. Furthermore, real-time data is shared by using an effective sharing mechanism to avoid redundancy of the collected information. However, dynamic route replanning and effective sharing mechanisms based on real-time data are still challenging problems. Therefore, the aim of this article is to describe route planning and data dissemination in real time using the Internet of Things paradigm. This objective is achieved by a novel data dissemination technique for information sharing among the roadside units in a hybrid VANET intelligent transportation system (Hybrid- VITS). Hybrid-VITS comprises VANETs, vehicular traffic servers, and a 5G-based cellular system of public transportation. By considering the traffic congestion in urban areas, the optimal path is calculated to replan routes based on the k shortest path algorithm, and a load balancing technique is adopted to avoid further congestion.

Environmentally friendly traffic control strategy - A case study in Xi’an city

Given the increasing painful repercussions of air pollution caused by massive vehicular emissions and the unsustainable development of transport sectors, the integration of traffic models with emission estimation models has been investigated in depth to evaluate the performance of the pre-determined environment-friendly transportation policies. Nevertheless, there is a lack of the optimization approach to design the traffic control strategy combining the coupled models. Therefore, an optimization framework is developed to optimize the traffic control strategies to mitigate the vehicular emissions in the considered network. The proposed framework integrates a macroscopic analytical traffic model with a macroscopic emissions estimation model to account for the vehicle emissions, and then genetic algorithm (GA) is employed to minimize vehicle emissions. A congested urban area in Xi’an city that consists of two signalized intersections is selected for a case study. The performance of the signal plans derived by the proposed framework is compared with the currently in use signal plans via the microscopic simulator AIMSUN. It indicates that the proposed traffic control scheme reduces expected vehicle trip travel time and vehicle emissions of four sorts of pollutants for the considered area. This optimization framework helps traffic operators to design environment-friendly traffic signal control strategy.

Evaluating the impact of new congestion charging scheme using smartphone-based data: a spatial change detection study

Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran’s I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change.

Optimal design of promotion based demand management strategies in urban rail systems

Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies.

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

Improving emergency evacuation planning with mobile phone location data

Timely responses to emergencies are critical for urban disaster and emergency management, particularly in densely populated mega-cities. Researchers and personnel involved in urban emergency management nowadays rely on computers to carry out complex evacuation planning. Agent-based modeling, which supports the representation of interactions among individuals and between individuals and their environments, has become a major approach to simulating evacuations wherein spatial–temporal dynamics and individual conditions need attention, such as congestion in urban areas. However, the development of optimal evacuation plans based upon agent-based evacuation simulations can be very time-consuming. In this study, to shorten the computation time to provide a timely response in an efficient way, we develop a knowledge database to store evacuation plans for typical population distributions generated by mobile phone location data. Subsequently, we use the prepared knowledge database (offline) to accelerate real-time (online) processes in searching for near-optimal evacuation plans. Our experimental result demonstrates that the evacuation plans generated with a knowledge database always outperform those that are generated without a knowledge database. Specifically, the knowledge database can reduce the computation time by an average of 96.76%, with an average fitness value improvement of 21.86%. This result confirms the effectiveness of our proposed approach in improving agent-based evacuation planning. With the rapid development of human sensor data collection and analysis, the estimation of a more accurate population distribution will become easier in future. Thus, we believe that the proposed approach of developing a knowledge database based on population distribution patterns will provide a more feasible alternative solution for evacuation planning in the practice of urban emergency management.

Analisis Pengaruh Riba Terhadap Tingkat Kemacetan di Ibu Kota Jakarta

The first and foremost reason riba is haram is because Allah declared it so. It is based on Allah’s infinite wisdom that He deems an action to be obligatory or prohibited, prescribing matters in man’s best interests, in this life and in the Hereafter, as He is the All-Wise, All- Knowing. The scholars of Islam have suggested the following reasons as to why riba is haram:There are many proofs showing that riba is haram and one of the most serious sins in Islam.Abu Hurayra said, the Messenger of Allah (pbuh) said: A time will come over mankind when not a single person will remain who does not consume riba (usury, interest); and if one does not consume it, the vapor or dust of it will reach him.nowdays Living in a capitalist society makes it difficult to completely avoid riba.however, the threat is not enough for the community to avoid the usury case. the effect of usury in real terms must be clearly described, in order to raise public awareness.the consumption behavior of motor vehicles is one of the causes of congestion in urban areas, many people use usury (Leasing) to buy vehicles, There are many reasons why we suffer traffic jams every day. One of these is the rapid growth of the population. As a result of this, the number of cars is increasing annually. Jakarta was named the world city with the worst traffic in one index last year. An estimated 70% of the city’s air pollution comes from vehicles. this paper found that congestion in Jakarta is caused by usury, this can be seen from the high use of usury instruments in motor vehicle financing (leasing).

Bicycle superhighway: An environmentally sustainable policy for urban transport

Abstract Bicycle is a sustainable low-carbon transport mode. However, insufficient or unplanned infrastructure leads to decrease in the share of bicycle in many cities of developing nations. In order to increase the bicycle share and to provide safer, faster and more direct routes, a bicycle superhighway is proposed for urban areas. This study identifies the potential of increase in the bicycle share. For maximum utilization of the new infrastructure, an algorithm is presented to identify the optimum number and locations of the connectors between proposed new infrastructure and existing network. Household income levels are incorporated into the decision making process of individual travellers for a better understanding of the modal shift. A real-world case study of Patna, India is chosen to show the application of the proposed superhighway. It is shown that for Patna, the bicycle share can escalate as high as 48% up from 32% by providing this kind of infrastructure. However, together with bicycles, allowing motorbikes on the superhighway limits the bicycle share to 44%. The increase in bicycle share is mainly a result of people switching from motorbike, public transport and walk to the bicycle. Further, to evaluate the benefits of the bicycle superhighway, this study first extends an emission modelling tool to estimate the time-dependent, vehicle-specific emissions under mixed traffic conditions. Allowing only bicyclists on the superhighway improves congested urban areas, reduces emissions, and increases accessibility. However, allowing motorbikes on the superhighway increases emissions significantly in the central part of the urban area and reduces accessibilities by bicycle mode to education facilities which are undesirable. This study elicits that a physically segregated high-quality bicycle superhighway will not only attract current non-cyclist travellers and increase the share of the bicycle mode, but will also reduce negative transport externalities significantly.

Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations

Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentrations. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOx) increased with higher CAVs. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

Multicriteria Approach to Sustainable Transport Evaluation under Incomplete Knowledge: Electric Bikes Case Study

The problem of sustainable city transport is a growing field of study, and will be addressed in this paper. With the rising significance of present transportation systems’ negative externalities on the environment, such as the unavoidable increase of air pollution levels, cities seek sustainable means of transport and reduction of combustion cars’ utilization. Moreover, improvements in the area of renewable energy sources have led to rising trends in sustainability, driving the usage and production of electric vehicles. Currently, there is an increasing tendency of looking for more sustainable transport solutions, especially in highly congested urban areas. It seems that in that case, electric bicycles can be a good option, as they yield more benefits in comparison to cars, especially combustion cars. In this paper, we identify an assessment model for the selection of the best electric bicycle for sustainable city transport by using incomplete knowledge. For this purpose, the Characteristic Objects METhod (COMET) is used. The COMET method, proven effective in the assessment of sustainable challenges, is a modern approach, utterly free of the rank reversal phenomenon. The evaluated model considers investigated multiple criteria and is independent of chosen alternatives in the criteria domain. Hence, it can be easily modified and extended for diverse sets of decisional variants. Moreover, the presented approach allows assessing alternatives under conditions of incomplete knowledge, where some data are presented as possible interval numbers.