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Congestion pricing policies (CPPs) are a common strategy for addressing urban traffic congestion. Research has explored several impacts of these policies (e.g., air quality, equity, congestion relief). The purpose of this review was to synthesize findings from publications examining CPP impacts on road user safety outcomes. We conducted a systematic search of relevant literature in four large research databases (Transport Research International Documentation, Web of Science, PubMed, and Scopus), searching from database inception through January 2021. We identified 18 eligible publications. Safety-related outcomes included overall crashes and injury crashes with stratification by injury severity and road user type (e.g., bicyclist, pedestrian). A majority of the publications examined zone-based CPPs (n = 13) and used observed data involving real policies (n = 10), as compared to a predicted or simulated analysis. Decreases in overall crashes and injuries for some road users were observed (e.g., car occupants). While some studies estimated short-term increases in injuries and crashes for bicyclists and motorcyclists (likely due to shifts from personal vehicle use to other transportation modes and increased exposure), most analyses focused on longer-term impacts and generally found a reversal and eventual decrease in injuries and crashes after a few years. The relative scarcity of safety outcomes in published literature, along with the wide breadth of CPP types, implementation contexts, and outcomes measured, demonstrates that more research on safety outcomes is needed. Cities and regions planning to implement CPPs should consider potential mode shifts and safety supports for all road users (e.g., bicycle and pedestrian infrastructure). [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the impacts of the economic context on traffic congestion and its consequential effects on private vehicle accessibility. We conduct a long-term analysis of spatiotemporal traffic congestion patterns in Madrid (Spain), comparing two urban realms: the 2008 economic crisis and the following post-crisis situation. We apply TomTom Speed Profiles data to assess daily variations in traffic congestion and their changes between both periods, and Twitter data to capture spatial patterns of the daily pulse of the city. Increased traffic, a by-product of economic recovery, resulted in higher congestion, particularly during peak hours. Nevertheless, these changes are spatially uneven. In the city core, an increase in congestion is relatively temporally homogeneous, while in the peripheral suburban zones, there has been only a marginal increase in travel times. On the other hand, in the urban outskirts, increased traffic congestion is more severe but visibly different between north and south. These differences have strong social connotations: over 40% of the population experienced a dramatic increase in travel times (more than 25%) during peak hours. Moreover, low-income groups are more likely to live in the more affected southern districts, suffering most the negative consequences of increased congestion. [ABSTRACT FROM AUTHOR]

Bengaluru, also considered India's Silicon Valley, has seen steady growth in population over the years. Bengaluru's rapid development has resulted in dwindling reservoirs, increased traffic congestion, high levels of air pollution, and, to some measure, a rise in summer temperatures. As a result of these changes in urban form over the last decade, anthropogenic heat fluxes for ozone production have increased. However, an observational study on the effects of growing urbanisation on trace gases in Bengaluru for various seasons and periods of the day is missing. Hence, in situ measurements of O3, NO, NO2, and NOX concentrations were carried out at Bengaluru, India, from January 2015 to December 2018. The data were examined for diurnal and interannual variations in trace gas mixing concentrations. The diurnal trend in O3 exhibits unimodal behaviour. Changes in photochemistry, local meteorology, and the planetary boundary layer's distinctive features cause a rise in the value of concentrations and lead to a peak. In contrast, the diurnal trend in NO, NO2, and NOX displayed a bimodal peak due to the combined effect of vehicular emissions and the planetary boundary layer. The link involving the oxidant OX (O3 + NO2) and NOx levels were investigated to determine the NOx-independent regional and NOx-dependent local contributions to OX in the atmosphere. Daytime contributions are higher than night-time contributions, according to the present study. The observed anomalies could be the consequence of photochemical processes that produce OX. [ABSTRACT FROM AUTHOR]

Path planning of intelligent driving vehicles in emergencies is a hot research issue, this paper proposes a new method of the best path selection for the intelligent driving vehicles to solve this problem. Based on the prior knowledge applied reinforcement learning strategy and the searching- optimized A* algorithm, we designed a hybrid algorithm to help intelligent driving vehicles selecting the best path in the traffic network in emergencies including limited height, width, weight, accident, and traffic jam. Through simulation experiments and scene experiments, it is proved that the proposed algorithm has good stability, high efficiency, and practicability. [ABSTRACT FROM AUTHOR]

Identification of the most relevant parameters of a sustainable transportation system is regarded as a significant choice that influences the success of policy-related intervention to improve efficiency. A hybrid-based approach has been used to analyze the data in multiple steps to improve accuracy and objectivity. In the first step, evidence on social, economic, and environmental indicators was formed for each opinion and then combined. The second step proposed a mathematical framework to treat indecision on the indicators collected from multiple sources, viz. expert and public opinion survey, models, and actual measurement during pre- and post-implementation stages of the Odd–Even scheme. By setting up, a reliability function and consistency index are used to evaluate and rank the Odd–Even transportation scheme introduced by the Government of Delhi. A case study in South Delhi's surrounding areas has been examined to demonstrate the proposed hybrid approach and evaluate the parameters in terms of the index. The result shows that the indicators like trip cost, trip time, safety and security, user's satisfaction, congestion level, parking demand, and fuel consumption affected positively by 1766.6, 22.9, 283.3, 19.1, 187.5, 200, and 17.2 percent, respectively, after the implementation of the scheme. The overall increase in the index was found as 28.2%, which favors the "Odd–Even" scheme as a sustainable transportation measure in Delhi. [ABSTRACT FROM AUTHOR]

The emerging IEEE 802.11ah is a promising communication standard for large-scale networks particularly the Internet of Things (IoT). The single-channel-based centralized channel access mechanism employed in 802.11ah does not scale well in such networks and leads to poor data reception quality. In this paper, we propose an Enhanced and Scalable Medium Access Control (ES-MAC) protocol, which employs multi-band sectorization and dynamic load balancing. These features facilitate multi-hop communication more efficiently and enhance network capacity. Traffic congestion issues prevailing around the access point node due to the large volume of uplink traffic is mitigated by allowing simultaneous transmission using multiple orthogonal channels and sectors. Simulation and analytical results establish the essence of the novel protocol by showing significant improvements in terms of throughput and average packet delay over the existing schemes. The proposed network architecture improves throughput and delay performance up to 150% and 100% respectively compared to the relevant schemes. [ABSTRACT FROM AUTHOR]

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data. [ABSTRACT FROM AUTHOR]

This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030. [ABSTRACT FROM AUTHOR]

Traffic congestion and accidents are more likely to occur on gently undulating roads called sags, because drivers are less likely to recognize a change in velocity. Therefore, to induce appropriate judgement and operation, we have proposed to bias the perception of velocity by tactile stimulation. Our previous study showed that the frequency of vibrotactile stimulation based on an engine is related to the perception of velocity and that an increase or decrease in frequency makes the driver feel they are traveling faster or slower than the actual velocity. In this study, to define the control initiation condition that biases the perception of velocity, we investigated a boundary model that can notice a change in velocity based on the velocity and the acceleration. Additionally, we investigated the effect of noticing a change in velocity and the driver's response by biasing the perception of velocity. Psychophysical experiments were conducted using a driving simulator that simulates the vibration of an actual vehicle. The results showed that the change in velocity required to notice an increase or decrease in velocity was 4 km/h when the accelerations were 0.02 and 0.04 m/s 2 , − 6 km/h when the acceleration was − 0.02 m/s 2 , and − 4 km/h when the accelerations were − 0.04 and − 0.06 m/s 2 . Furthermore, by biasing the perception of velocity, we demonstrated that the participants recognized a change in velocity on a gently undulating road and appropriately operated the acceleration pedal. [ABSTRACT FROM AUTHOR]

The in-situ stress has great influence on blasting damage during deep tunnel excavation. However, the effects of in-situ stress on the blasting damage during tunnel excavation are deficient, and its mechanism is not clear. Therefore, theoretical analyses and numerical simulations are adopted to study the rock excavation damage under the coupling effect of in-situ stress and blasting load. The results indicate that the damage extent has obvious correlation with in-situ stress level. The damage extent decreases first and then increases as the stress level rises. In the decreasing part, blasting load is the dominant factor and mainly causes tensile-shear damage. The rising of in-situ stress here raises the tensile-shear yield limit of surrounding rock, which leads to the reduction of damage extent. When it comes to increasing part, the secondary stress field induced by excavation takes place of blasting load while mainly causing compress-shear damage. The damage extent rises up with the in-situ stress level increasing continuously. For the circular tunnel in this paper, the turning point where the trend of damage extent turns to increasing from decreasing is in-situ stress of 12.5 MPa. [ABSTRACT FROM AUTHOR]

Intelligent Transportation System (ITS) is now being widely built all over the world. Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic information for travelers to save their time, but also assists traffic management agencies to manage the traffic system. We find that the existing papers, including neural networks, do not perform well due to the complex association features between the road sections. In addition, the lack of a better evaluation standard for traffic congestion also makes the effect of traffic congestion prediction worse. In this paper, we propose a method to generate traffic congestion index by mining free-stream speed and free-stream flow. Considering the association features of road segments in the road network, we propose a road segment grouping method based on association subgraph to pre-train deep learning model and realize information sharing between road segments. Combining the features of traffic data and CNN model, we propose a traffic congestion prediction model named SG-CNN which training process is optimized by road segment grouping algorithm. The experiments demonstrate that our proposed model has higher accuracy rate than other models. [ABSTRACT FROM AUTHOR]

Growth in multimedia traffic over the Internet increases congestion in the network architecture. Software-Defined Networking (SDN) is a novel paradigm that solves the congestion problem and allows the network to be dynamic, intelligent, and it centrally controls the network devices. SDN has many advantages in comparison to traditional networks, such as separation of forwarding and control plane from devices, global centralized control, management of network traffic. We design a policy-based framework to enhance the Quality of Service (QoS) of multimedia traffic flows in a potential SDN environment. We phrase a max-flow-min-cost routing problem to determine the routing paths and presented a heuristic method to route the traffic flows in the network in polynomial time. The framework monitors the QoS parameters of traffic flows and identifies policy violations due to link congestion in the network. The introduced approach dynamically implements policy rules to SDN switches upon detection of policy violations and reroutes the traffic flows. The results illustrate that the framework achieves a reduction in end-to-end delay, average jitter, and QoS violated flows by 24%, 37%, and 25%, respectively, as compared to the Delay Minimization method. Furthermore, the proposed approach has achieved better results when compared to SDN without policy-based framework and reduced end-to-end delay, average jitter, and QoS violated flows by 51%, 62%, and 28%, respectively. [ABSTRACT FROM AUTHOR]

The Nagel–Schreckenberg vehicular traffic model is extended to analyse a system of vehicles moving on a scale-free network to study extreme events in them. The dependence of the free-flow and congestion states on the density of vehicles is determined. In particular, a free-flow state at low vehicular density is observed and it gradually transitions to a congested state at higher density. Using detrended fluctuation analysis, it is shown that the flux of vehicles are long range correlated in a regime of low density. At higher densities, the flux becomes uncorrelated. We study the recurrence interval distribution for extreme events and the probability for its occurrence at low and high vehicular density regimes. It is shown that the occurrence probability for extreme events is independent of the degree of the node or the threshold used for defining extreme events [ABSTRACT FROM AUTHOR]

Urban traffic congestion and haze pollution have become the main obstacles to the development of most cities in emerging economies. It is not clear how urban traffic development processes impact on PM2.5 concentration for the cities of emerging economies. Motivated by exploring the relationship between urban traffic development and PM2.5 pollution, 30 provinces in China (a representative emerging economy) from 2007 to 2016 were taken as examples, and threshold regression model and geographically temporally weighted regression model were used to explore the nonlinear relationship and their spatio-temporal heterogeneity. These empirical researches demonstrated that the impact of urban traffic development on PM2.5 pollution has a significant threshold effect. That is, when the road area crosses the threshold, it will significantly aggravate the regional PM2.5 pollution. Meanwhile, regional economic development also shows a significant threshold effect. Moreover, the relationship between urban traffic development and PM2.5 pollution in various Chinese provinces presents significant spatial heterogeneity. Specifically, the Chinese provinces are divided into four categories, and urban planning should be designed for different types for the sustainable development of the economy and environment. Our results not only contribute to advancing the existing literature, but also merit particular attention from urban planners in emerging economies. [ABSTRACT FROM AUTHOR]

In this paper, we consider the interaction of elementary waves for the Aw–Rascle (AR) traffic flow model with variable lane width. The model is established based on the conservation laws where the lane width is considered, and the variable spaces ahead of the vehicles can be viewed as a source term which is incorporated into the model. Stationary waves are involved as the ingredient of the elementary waves. We then discuss the interactions of stationary wave with rarefaction wave, shock wave and contact discontinuity respectively, and construct the solutions globally in the phase plane. The results may contribute to dealing with traffic jam problems in the future study. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in the last few decades. Though the Government is taking various measures like installing more traffic signals, constructing fly-overs, one-ways, etc., the problem could not be addressed effectively on the heavily congested areas. The increasing number of vehicles, the dynamic population of traffic, the narrowness of roads and the absence of alternative roads or other routes are the major obstacles to reduce traffic congestion. Researchers of various streams have been working on predicting traffic volume to solve this problem. In this paper, a new method named Intuitionistic Fuzzy Grey-Markov Model(IFGMM) is introduced, which is an extension of Grey-Markov to Intuitionistic fuzzy set. The model is distinctive when compared with other models as it has a high level of accuracy performance. The forecasting result of this model is almost in proximity with the actual result of the traffic volume. In addition, the relative errors shown in this method are lesser than that of other models, due to its prediction accuracy is higher. Since the data collected in this method are taken from a heavily congested road from a metropolitan city, the proposed approach will be suitable for any congested area of the world. [ABSTRACT FROM AUTHOR]

The hub location problems deal with determining the optimal location of hub facilities and allocating the demand nodes to these hubs in such a way that the traffic between any origin–destination pair is routed effectively. This paper proposes the uncapacitated r-allocation p-hub median problem under congestion. The problem is formulated as a second-order cone programming and an efficient simulated annealing heuristic algorithm is proposed to solve the large instances of the problem. Extensive computational experiments are conducted based on three well-known data sets to demonstrate the efficiency of the proposed algorithm and also to study the effect of different input parameters on the optimal solutions. Some managerial insights are derived based on the obtained numerical results. [ABSTRACT FROM AUTHOR]

Bursty traffic and thousands of concurrent flows incur inevitable network congestion in datacenter networks (DCNs) and then affect the overall performance. Various transport protocols are developed to mitigate the network congestion, including reactive and proactive protocols. Reactive schemes use different congestion signals, such as explicit congestion notification (ECN) and round trip time (RTT), to handle the network congestion after congestion arises. However, with the growth of scale and link speed in datacenters, reactive schemes encounter a significant problem of slow responding to congestion. On the contrary, proactive protocols (e.g., credit-reservation protocols) are designed to avoid congestion before it occurs, and they have the advantages of zero data loss, fast convergence and low buffer occupancy. But credit-reservation protocols have not been widely deployed in current DCNs (e.g., Microsoft, Amazon), which mainly deploy ECN-based protocols, such as data center transport control protocol (DCTCP) and data center quantized congestion notification (DCQCN). And in an actual deployment scenario, it is hard to guarantee one protocol to be deployed in every server at one time. When credit-reservation protocol is deployed to DCNs step by step, the network will be converted to multi-protocol state and will face the following fundamental challenges: 1) unfairness, 2) high buffer occupancy, and 3) heavy tail latency. Therefore, we propose Harmonia, aiming for converging ECN-based and credit-reservation protocols to fairness with minimal modification. To the best of our knowledge, Harmonia is the first to address the trouble of harmonizing proactive and reactive congestion control. Targeting the common ECN-based protocols—DCTCP and DCQCN, Harmonia leverages forward ECN and RTT to deliver real-time congestion information and redefines feedback control. After the evaluation, the results show that Harmonia effectively solves the unfair link allocation, eliminating the timeouts and addressing the buffer overflow. [ABSTRACT FROM AUTHOR]

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of '5' phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study conducted a driving simulation experiment to compare four automated driving systems (ADS) designs during lane change demanding traffic situations on highways while accounting for the drivers' gender, age, experience, and practice. A lane-change maneuver was required when the automated vehicle approaches traffic congestion on the left-hand lane. ADS-1 can only reduce the speed to synchronize with the congestion. ADS-2 reduces the speed and issues an optional request to intervene, advising the driver to change lanes manually. ADS-3 offers to overtake the congestion autonomously if the driver approves it. ADS-4 overtakes the congestion autonomously without the driver's approval. Results of drivers' reaction, acceptance, and trust indicated that differences between ADS designs increase when considering the combined effect of drivers' demographic factors more than the individual effect of each factor. However, the more ADS seems to have driver-like capacities, the more impact of demographic factors is expected. While preliminary, these findings may help us understand how ADS users' behavior can differ based on the interaction between human demographic factors and system design. [ABSTRACT FROM AUTHOR]

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users' flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users' QoS requirements. [ABSTRACT FROM AUTHOR]

For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns. [ABSTRACT FROM AUTHOR]

Recent studies proposed parking reservation schemes in a bi-modal transport network to manage parking competition and traffic congestion. This study proposes a new flexible parking reservation scheme, under which commuters' reservation can expire and those arriving later than the reservation expiration time can retain his reservation by paying additional late-for-reservation fees. Time-varying late-for-reservation fees are also examined. The proposed reservation scheme is more flexible and practical than those in the literature. Moreover, we found that, when compared to reservation scheme in the literature, the total social cost can be further reduced by an appropriately designed flexible reservation scheme in this paper. We also analytically and numerically quantify the efficiency gain of the proposed flexible parking reservation scheme. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 has made a global catastrophic situation that caused 1,039,406 deaths out of 35,347,404 infections, and it will also cause significant socio-economic losses with poverty increasing from 17.1 to 25.9%. Although the spreading rate of COVID-19 is very high on October 6, 2020, the death rate is still less than 2.94%. Nonetheless, this review article shows that the lockdown has induced numerous positive impacts on the environment and on energy consumption. For instance, the lockdown has decreased the electricity demand by 30% in Italy, India, Germany, and the USA, and by 12–20% in France, Germany, Spain, India, and the UK. Additionally, the expenditure of the fuel supply has been decreased by 4% in 2020 as compared to the previous years (2012–2019). In particular, The global demand for coal fuel has been reduced by 8% in March and April 2020 as compared to the same time in 2019. In terms of harmful emissions, the lockdowns reduced the emissions of nitrous oxides by 20–30% in China, Italy, France, Spain, and by 77.3% in São Paulo, Brazil. Similarly, the particulate matter level has been reduced from 5–15% in Western Europe, to 200% in New Delhi, India, which in turn has enhanced the air quality in a never-seen manner in recent times. In some places, such as New York, USA, CO2 emission was also reduced by 5–10%. The water quality, in several polluted areas, has also been remarkably enhanced, for example, the dissolved oxygen content in the Ganga River, India, has increased by about 80%. Traffic congestion has also been reduced worldwide, and in some areas, it has been reduced by 50%, such as New York and Los Angeles, USA. Overall, while the COVID-19 pandemic has shrinked the global economy by 13–32%, the pandemic has also clearly benefited to other sectors, which must be considered as the spotlight for the permanent revival of the global ecosystem. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) cell frameworks are relied upon to encounter colossal traffic congestion from mobile devices. To decrease network congestion, the 5G cell systems need to be changed to accommodate the soaring traffic demands from these devices. In a 5G network, a cloud-based radio access network (C-RAN) has a significant role to increase the data rate. With the support of C-RAN, traffic congestion in the 5G network is handled by presenting the Call Admission Control (CAC) technique. Although this CAC technique improves the system efficiency, maximum of call blocking probability due to the traffic congestion is the main challenge. So, the solution to this challenge is introducing the optimal call admission decision maker. In this paper, Artificial Fish Swarm Algorithm based Fuzzy Inference System (FIS-AFSA) is proposed as a decision maker. Using AFSA algorithm, the fuzzy parameters are optimized in this strategy. In this approach, few delay tolerant connections are outsourced from private cloud to public cloud with certain price when congestion. The proposed FIS-AFSA based CAC scheme's performance outperforms that of the Fuzzy based CAC technique in the basis of call blocking probability, throughput and resource utilization as show in simulation results. [ABSTRACT FROM AUTHOR]

Public transport plays an essential role in helping people escape from the congested traffic in large and crowded cities. Evaluating customer satisfaction is the key to the continuous quality improvement of public transport services. This work proposes a new public transport customer satisfaction evaluation approach based on an extended thermodynamic method with q-rung orthopair fuzzy sets. First, we use the q-ROFSs to handle the ambiguity and uncertainty of customer satisfaction evaluation information for public transport. Then, we extend the thermodynamic method to determine the customer satisfaction levels of public transport lines. Moreover, the stepwise weight assessment ratio analysis method is utilized to specify the weights of evaluation criteria as it is simple and time-saving. The effectiveness of the proposed approach is illustrated with a customer satisfaction evaluation of the rail transit network in Shanghai, China. Results showed that Line 7 has the highest customer satisfaction and Line 2 has the lowest customer satisfaction among the considered rail transit lines. Besides, nine evaluation criteria need to be optimized to improve the customer satisfaction level of Line 2 and the most critical one is train crowding. [ABSTRACT FROM AUTHOR]

Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era. [ABSTRACT FROM AUTHOR]

The spatial characteristics of residents' leisure activities not only reflect their demand for urban leisure space but also affect the urban spatial layout. This study takes Shenyang, China as an example and analyzes the characteristics of residents' leisure activities through questionnaires. On this basis, it uses point of interest data and mobile phone signaling data to identify various types of residential and leisure functional relationships, and uses spatial analysis and community detection to assess the distance characteristics, flow patterns, and community structure of residents' leisure activities, so as to discuss the spatial structure of residents' leisure activities in Shenyang. The results showed that: 1) in addition to leisure at home, Shenyang residents mainly went to shopping malls, supermarkets, and parks for leisure activities, and the proportions of residents of the two types of leisure activities were approximately equal; 2) the average distances that residents traveled for shopping and park leisure were near in the middle and far in the periphery, and the travel costs of peripheral residents for centrally located leisure were higher than those for residents in central areas; 3) the flow patterns of the residential-shopping and residential-park functional relationships displayed clustering mode characteristics, and Shenyang presented a significant monocentric structure; and 4) residents' shopping activities were concentrated in the southern community, and walking in the park activities were concentrated in the western community. Residents' leisure activities were characterized by centripetal agglomeration, which was prone to problems such as traffic congestion and big city diseases. The spatial expansion process in the city was characterized by obvious directional inheritance and path dependence, and the construction of sub-cities is needed to improve the related service facilities. [ABSTRACT FROM AUTHOR]

Accurate prediction of surface deformation due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This task is further compounded by the growing trend for coal to be extracted under buildings by leaving coal pillars in place. To rationalize the design of mining schemes to ensure effective control of surface deformation, precise 3D numerical models of different mining schemes were developed in this paper. Two schemes were compared and analyzed based on the results of numerical simulations. Also, to verify the reliability of the prediction results from numerical simulations, the probability integral method was used for this prediction. The results show that the prediction results of the numerical simulations are close to those calculated by the probabilistic integral method, which proves the reliability of the numerical simulation prediction. The optimal mining scheme was determined by comparing the predicted results from the analysis of theoretical calculations and numerical simulations. [ABSTRACT FROM AUTHOR]

Exploiting the full potential of pedestrian infrastructure is becoming critical in many environments which cannot be easily expanded to cope with the increasing pedestrian demand. This is particularly true for train stations as in many dense cities space is limited and expansion is difficult and very costly. In this paper, we investigate how to improve the level-of-service experienced by pedestrians by regulating and controlling their movements with a dynamic traffic management system. Although dynamic traffic management systems have been widely investigated in the last two decades to mitigate vehicular traffic congestion, little attention has been given in the literature to dynamic traffic management systems for pedestrian flows. The objective of this paper is to develop the concept of a dynamic traffic management system for pedestrian flows by building on the experience acquired from vehicular traffic management systems. We first propose a general framework for dynamic traffic management systems which takes into account the specificities of pedestrian traffic. The specificities of pedestrian traffic are discussed and emphasized. Then we illustrate the framework by using a control strategy designed for pedestrian flows that mitigates the issues induced by bidirectional flows. We show the effectiveness of this strategy by simulating a subpart of the train station in Lausanne (Switzerland). The results show a substantial improvement despite the relative simplicity of the method. These results emphasize the under-explored potential of pedestrian control and guidance when integrated into a dynamic pedestrian management system. [ABSTRACT FROM AUTHOR]

In recent times, congestion in traffic is bit cost effective and several traffic management methods are utilized to balance the traffic as per the capacity of roads. However, many methods fails in pre-analyzing the network events to regulate the traffic flow. Also, the dynamic movement of vehicles fails in adapting the vehicle units to follow the changing topology. Hence, it is necessary for packet forwarding between source and destination nodes in a precise manner, where the packet loss and communication overhead should be in control. In this paper, we utilize the concept of Connected Dominating Set (CDS) approach in traffic management for reducing the flow of traffic streaming in main routes. The CDS approach dynamically adapts itself with the changing topology and acts as a virtual backbone to reduce traffic overhead. It restricts the increased flow of vehicles to the dominators, since traffic congestion in such areas increases. The CDS forwarding (CDSF) finds the dominating or high congestion routes and diverts the traffic to other areas based on the level of traffic in sub-routes. The CDSF method works on an assumption that edges on road segments has multiple vehicles, where it increases the network efficiency. However, we make careful consideration of choosing the other road segments to traverse the required packets from source to destination with reduced packet loss. The construction of a virtual backbone network with series of neighboring backbone nodes traverses efficiently the packets. Further, choosing vehicles with low velocity improves the probability of connectivity in a specific area and increases the network throughput. This improves the re-routing flexibility and makes the packets to reach the destination without traffic congestion in VANETs. The CDSF method and control strategies are tested in NS2 and SUMO simulator under different traffic conditions. The results of simulation show that CDSF traffic management system reduces significantly the traffic congestion and overall traffic cost that includes CDF, average latency, average end-to-end delay, Hop count, packet error rate, percentage delivery ratio, data delivery rate, throughput. The result shows that the proposed method 0.345 times improved packet delivery ratio, 0.35 times improved throughput and 0.7 times reduced delay than the existing methods. Further, the CDSF method controls the traffic density by reducing the maximum queue length and traffic controlling time. [ABSTRACT FROM AUTHOR]

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice. [ABSTRACT FROM AUTHOR]

A comprehensive and satisfying route- guidance system is considered valuable for citizens and tourists worldwide. An effective route-guidance system is essential because of the sophisticated city formation and transportation system in India. For better utilization of route-guidance systems. It needs to be easy to use and informational but to be more sensible to give optimal route options in terms of low cost and low time in various situations (Such as climate conditions, traffic jam, road structure). This paper aims to comprehend the research trend in the route guidance system and easily observe the various technique of route guidance. Different impressive findings have come out of this study, supporting current and future researchers to evaluate and set their research roadmap. This paper also explains the existing approaches for route guidance systems in simple language, which will be fruitful for researchers. Furthermore, this paper also envisions the future of RGs, which may open up new research directions in this domain. [ABSTRACT FROM AUTHOR]

PM10 and PM2.5 concentrations in Ljubljana city bus were monitored during entire shift, and individual particles were morphologically and chemically characterised in order to determine PM concentration variability, particle sources, solubility in simulated pulmonary environment and effects on human health. PM measurements revealed high mean PM10 (82.8 μg/m3) and PM2.5 (47 μg/m3), which were highest and most variable during rush hours with fluid traffic and lowest during traffic jams with standing vehicles. Individual particle analysis showed that airborne particles were dominated by metal-bearing phases, particularly small-sized (Cr,Mn,Zn)-bearing Fe-oxyhydroxides and Al-/Fe-Al-oxides, large (Fe,Cr,Ni)- and (Cu,Zn,Ni)-alloys, and small-sized Sb-sulphide and Ba-sulphate. Non-metallic phases were represented by (Ca,Mg)-carbonates, Al-silicates, Na-chloride and Ca-sulphate. Comparison with possible source materials (vehicle exhaust emissions, brake disc dust and road sediment) showed that primary sources of these metal-bearing phases were wear of brake discs, brake pads and tyres, and also wear of engine components and catalytic converters. Most non-metallic phases originated from resuspension of road sediment, containing road sanding materials, but also from emissions of burned fuel and lubricating oil (Ca-sulphate). Assessment of effects on human health indicated that mean PM concentrations, which significantly exceeded daily limit values, increased mortality (by 2–3%) and morbidity (by 7–8%) risk for bus drivers. Simplified PHREEQC calculations of airborne metal-bearing phase solubility in aqueous solutions simulating pulmonary environment showed that metallic Fe, Ba-sulphate, Sb-sulphide and Al-oxide, partly also Cu-bearing metal alloys, were soluble under reducing and oxidising conditions, but released metals were removed from solution by precipitation of stable secondary metal-bearing phases. [ABSTRACT FROM AUTHOR]

Emergency vehicles (EVs) are significant in disseminating sensitive information across the road-side communication networks. This amalgamation of vehicles and communication networks improves the reachability and accessibility of sensitive data along the driving scenario. However, the communication network experiences data and traffic congestion due to wireless medium and varying vehicle densities. In order to address the problem of data and traffic congestion, this paper introduces message dissemination with re-route planning (MDRP) method. This method initiates a dissemination boundary for selecting neighbors and weight for selecting the re-routing path. The weight is based on the traffic conditions of the road segment along with consideration of the timeout of the EV message. The joint process of rerouting and data transmission is supported by dependent queue management for improving the message delivery and reducing the impact of delaying instances in the travelling path. [ABSTRACT FROM AUTHOR]

While instruments to price congestion exist since the 1970s, less than a dozen cities around the world have a cordon or zone pricing scheme. Geneva, Switzerland, may be soon joining them. This paper builds on a detailed review of the existing schemes to identify a set of plausible design options for the Geneva congestion charge. In turn, it analyzes their acceptability, leveraging a large survey of residents of both Geneva and the surrounding areas of Switzerland and France. Our original approach combines a discrete choice experiment with randomized informational treatments. We consider an extensive set of attributes, such as perimeter, price and price modulation, use of revenues, and exemption levels and beneficiaries. The informational treatments address potential biased beliefs concerning the charge's expected effects on congestion and pollution. We find that public support depends crucially on the policy design. We identify an important demand for exemptions, which, albeit frequently used in the design of environmental taxation, is underexplored in the analysis of public support. This demand for exemptions is not motivated by efficiency reasons. It comes mostly by local residents, for local residents. Further, people show a marked preference for constant prices, even if efficiency would point to dynamic pricing based on external costs. Hence, we highlight a clear trade-off between efficiency and acceptability. However, we also show, causally, that this gap can in part be closed, with information provision. Analyzing heterogeneity, we show that preferences vary substantially with where people live and how they commute. Even so, we identify several designs that reach majority support. [ABSTRACT FROM AUTHOR]

In general, WMSN follows many-to-one technique to transmit the data and to sense the information. There occurs a rapid increase in congestion or network traffic due to the generation of a large number of sensors. Moreover, the performances are jeopardized due to transmission and a high rate of packet losses. In order to address such shortcomings, this paper aims in developing a Hybrid Red Deer Salp Swarm (HRDSS) based routing approach. The HRDSS approach is the integration of a red deer and the salp swarm optimization algorithm. The work outlined in this paper is to minimize four different objectives namely packet loss, memory, delay and expected transmission cost. The main intention of the multi-objective function involves generating a diverse optimal solution set that is utilized to evaluate the trade-off among various objectives. We also presented the simulation results for two different scenarios comprising of the network grid and the optimization test functions that are carried out to determine the effectiveness of the system. In addition to this, the comparative analysis is done and the results reveal that the proposed HDRSS approach provides the best optimal routing path when compared with various approaches. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) can be pivotal factor towards realization of Intelligent Transportation Systems. IoV principle focus is to have time decisive safety applications, optimize traffic flow, infotainment and Vehicular network with the intention to improve road safety through deployment of application allowing drivers to anticipate danger on the road. One of the important challenges of IoV is timely, reliable, and consistent propagation of messages among vehicles which enable drivers to take appropriate decisions to have improved road safety. Many proposals has been put forward by researchers to identify the traffic jam and routing the vehicular nodes in urban and highway roads for consistent, safe and secured driving environment. Even though the protocols have several limitations including lack of scalability to larger networks, routing overheads, etc. To overcome these limitations bio-inspired, big data, genetic algorithm, machine learning approaches have been proposed to identify and route packets among vehicular nodes in an optimized manner. The paper contains the survey of already proposed method and new approach to identify and route the vehicular node for the IoV environment. [ABSTRACT FROM AUTHOR]

Modern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms. [ABSTRACT FROM AUTHOR]

To investigate highway petrol station replenishment in initiative distribution mode, this paper develops a mixed-integer linear programming (MILP) model with minimal operational costs that includes loading costs, unloading costs, transport costs and the costs caused by unpunctual distribution. Based on discrete representation, the working day is divided into equal time intervals, and the truck distribution process is decomposed into a pair of tasks including driving, standby, rest, loading and unloading. Each truck must execute one task during a single interval, and the currently executing task is closely related to the preceding and subsequent tasks. By accounting for predictive time-varying sales at petrol stations, real-time road congestion and a series of operational constraints, the proposed model produces the optimal truck dispatch, namely, a detailed task assignment for all trucks during each time interval. The model is tested on a real-world case of a replenishment system comprising eight highway petrol stations, one depot, one garage and eight trucks to demonstrate its applicability and accuracy. [ABSTRACT FROM AUTHOR]

Rapid urbanization has meant that cities around the world must deal with problems like traffic congestion, aging infrastructure, affordable housing, and climate change. Increasingly, policymakers are turning to investments in technology and digital infrastructure to address these problems. Yet the move towards so-called smart cities is not simply responsive, and policymakers increasingly advocate for smart city initiatives as a necessary step towards objective, efficient, and rational governance. This understanding of technological interventions as inherently progressive, however, causes many to overlook the erasures, biases, and limitations that emerge from trying to leave the past behind. As the problems associated with this enthusiasm become more apparent, the smart city movement must therefore recalibrate its relationship to not just technology but time itself. Building on deconstructive temporalities emerging out of quantum physics, I argue that cities must begin drawing from alternative temporalities more open to the intersections between past, present, and future. As such, I suggest that the time has come to replace the ideal of the smart city with that of the spectral city – an incomplete city haunted by the ghosts (and composts) of the past. [ABSTRACT FROM AUTHOR]

This study attempts to estimate the cost of traffic congestion in Dhaka Metropolitan City of Bangladesh through collecting field level primary data. Traffic congestion is an important issue in the city. The study findings indicate that most of the commuters in the city travel more than 2 h/day. The calculated travel-related indices indicate that the commuters in the city have to spend more time on the road compared to an uncongested situation. They lose about one working hour per day on an average due to traffic congestion. Combining the elements of traffic congestion, this study finds that the calculated cost of traffic congestion crosses the benchmark of US$ 4 per day per commuter in the city. According to the perception of the commuters, pick up/drop off of passengers at any place is the most important cause, while ambulance stacking is the most severe impact of traffic congestion in the city. The commuters perceive more use of public transport as the prime solution to tackle the traffic congestion problem in the city. [ABSTRACT FROM AUTHOR]

The exponential growth in the number of internet users for utilizing diversified applications, such as Voice over IP(VoIP), video on demand (VoD), video streaming and e-learning, has resulted in a steady increase in traffic on the transmission channel. Despite video streaming being a large contributor of traffic to both commercial and non-commercial uses, it still lags in the proper delivery of data to the users of cellular devices and another heterogeneous network. As the popularity of video streaming services increases, so does the user's expectation for quality services. The poor quality of video streaming services has now become absolute in the video stream ecosystem. Therefore, video service providers have taken providing good Quality of Service to their users as their major goal. Provisioning of optimal QoS services in these applications become a challenging task for service providers in the presence of huge traffic and congestion. Since video streaming architecture follows a server-client architecture model, we propose to optimize the functions at the server-side to maximize QoS. The performance of the proposed architecture is compared with the existing architecture in terms of QoS features such as delay, delivery ratio and throughput for various data rates and buffer sizes. The proposed work is simulated using NS2 Simulator. The results show that, end to end delay is reduced by 10%, the delivery ratio is improved by 12% and throughput is improved by 17% when compared to the existing architecture. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]

In a traffic flow, once congestion is initiated, the throughput to the downstream of the bottleneck tends to decrease from the maximum throughput in the free-flow state. It is known that the maximum throughput in the free-flow state varies according to the traffic conditions for the same study site. If we can correctly forecast the maximum throughput depending on the traffic conditions, traffic congestion can be prevented or mitigated through ramp-metering. In this study, the relationship between the maximum throughput and the on-ramp ratio is analyzed. It turns out that maximum throughput of each study site has different values with the change of on-ramp ratio and there exists a convex quadratic-polynomial relationship between them. The elasticity of the maximum throughput to the on-ramp ratio is influenced by the geometric highway design and operation factors. The results of this study confirm that the on-ramp ratio control can effectively minimize the capacity drop and prevent traffic congestion. [ABSTRACT FROM AUTHOR]

Tremendous increase in the use of the mobile devices equipped with the GPS and other location sensors has resulted in the generation of a huge amount of movement data. In recent years, mining this data to understand the collective mobility behavior of humans, animals and other objects has become popular. Numerous mobility patterns, or their mining algorithms have been proposed, each representing a specific movement behavior. Convoy pattern is one such pattern which can be used to find groups of people moving together in public transport or to prevent traffic jams. A convoy is a set of at least m objects moving together for at least k consecutive time stamps where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns do not scale to real-life dataset sizes. Therefore in this paper, we propose a generic distributed convoy pattern mining algorithm called DCM and show how such an algorithm can be implemented using the MapReduce framework. We present a cost model for DCM and a detailed theoretical analysis backed by experimental results. We show the effect of partition size on the performance of DCM. The results from our experiments on different data-sets and hardware setups, show that our distributed algorithm is scalable in terms of data size and number of nodes, and more efficient than any existing sequential as well as distributed convoy pattern mining algorithm, showing speed-ups of up to 16 times over SPARE, the state of the art distributed co-movement pattern mining framework. DCM is thus able to process large datasets which SPARE is unable to. [ABSTRACT FROM AUTHOR]

Previous research on the role of commute duration in subjective well-being (SWB) has paid little attention to developing countries and the possible pathways determining the relationship between them. In this study, we construct a conceptual framework, identifying the possible pathways through which commute duration may affect SWB. Next, we empirically analyse some of these pathways in the context of urban China. We find that although the direct effects of commute duration on life satisfaction and emotional well-being are insignificant, prolonged commute duration has significant and negative indirect effects on life satisfaction and emotional well-being through lowering health, job satisfaction, and community-based social capital. In addition, compared with people who commute by public transport, those who use private cars are more satisfied with their lives. Urban policymakers should give more consideration to reducing traffic congestion, to promoting the housing and labour market, as well as public transport, to reduce the negative influences of commute duration on SWB. [ABSTRACT FROM AUTHOR]

Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most serious traffic congestion occurs, implementation of a single TDM measure might not be enough to reduce congestion, because the congestion mechanism in this case is highly complex and involves different transportation modes interacting with each other. Implementation of multiple TDM measures has rarely been discussed in the literature. Therefore, in this study, we propose a simulation-based joint optimization framework composed of dedicated bus lanes and vehicular congestion pricing. The objective of the optimization process is to minimize the congestion cost based on an advanced macroscopic flow theory called the multimodal macroscopic fundamental diagram (mMFD), which can capture the macroscopic traffic dynamics of multimodal transportation systems. In the proposed framework, we develop mMFD-based congestion pricing scheme and incorporate traveler's behavioral model (i.e. joint departure time and mode choices) with the microscopic traffic simulator. We consider the Tokyo central area as a case study. The simulation results indicate that space allocation of 4.7% for the dedicated bus lanes would be optimal for Tokyo's network, while the optimal congestion pricing scheme indicates that charges of 900 JPY between 7:30 and 8:00 AM and 300 JPY between 8.00 and 8:30 AM should be levied. [ABSTRACT FROM AUTHOR]

Air pollution has worsened as a result of increased traffic congestion in cities. Using air pollution caused by motor vehicle emissions (mainly by carbon monoxide, hydrocarbon, nitrogen oxide, and particulate matter) as an example, in this study, we applied an integrated algorithm comprising system dynamics, entropy weight method, and gray system theory to establish a weighted logic function. A vehicle pollutant control model (including the transport, health, and environmental subsystems) was established by VENSIM software. The medium- and long-term key variable analysis showed that the integrated algorithm, fully reflecting the advantages of system dynamics and gray system theory, more accurately described air pollution caused by vehicle emissions. Our research results also revealed that the combined strategy of penalties and air pollution charging fee had a threefold effect: reduced congestion and emissions, improved health impact index, and reduced number of illegal trips. Specifically, the degree of traffic congestion, number of illegal trips, and the degree of air pollution decreased by approximately 82.40%, 69.35%, and 68.91%, respectively, whereas the health impact index increased by about 279.03%. This finding provides guidance for improving policy and optimizing management and control modes. [ABSTRACT FROM AUTHOR]