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Authoritative, trustworthy, continual, automatic hourly air quality monitoring is a relatively recent innovation. The task of reliably identifying long-term trends in air quality is therefore very challenging, as well as complex. Ports are major sources of atmospheric pollution, which is linked to marine traffic and increased road traffic congestion. This study investigated the long-term trends and drivers of atmospheric pollution in the port cities of Houston, London, and Southampton in 2000–2019. Authoritative air quality and meteorological data for seven sites at these three locations were meticulously selected alongside available traffic count data. Data were acquired for sites close to the port and sites that were near the city centre to determine whether the port emissions were influencing different parts of the city. Openair software was used for plots and statistical analyses. Pollutant concentrations at Houston, Southampton and Thurrock (London) slowly reduced over time and did not exceed national limits, in contrast to NO2 and PM10 concentrations at London Marylebone Road. Drivers of atmospheric pollution include meteorology, geographical and temporal variation, and traffic flow. Statistically significant relationships (p < 0.001) between atmospheric pollution concentration and meteorology across most sites were found, but this was not seen with traffic flows in London and Southampton. However, port emissions and the other drivers of atmospheric pollution act together to govern the air quality in the city. [ABSTRACT FROM AUTHOR]

When imposing traffic congestion pricing around downtown commercial centers, there is a concern that commercial activities will have to consider relocating due to reduced demand, at a cost to merchants. Concerns like these were important in the debates before the introductions of congestion charges in both London and Stockholm and influenced the final policy design choices. This study introduces a sequential experimental game to study reactions to congestion pricing in the commercial sector. In the game, merchants first make location choices. Consumers, who drive to do their shopping, subsequently choose where to shop. Initial responses to the introduction of congestion pricing and equilibrium selection adjustments over time are observed. These observations are compared to responses and adjustments in a condition where congestion pricing is reduced from an initially high level. Payoffs are non-linear and non-transparent, making it less than obvious that the efficient equilibrium will be selected, and introducing possibilities that participants need to discover their preferences and anchor on past experiences. We find that initial responses reflect standard inverse price–demand relations, and that adjustments over time rely on signaling by consumers leading to the efficient equilibrium. There is also evidence that priming from initial experiences influence play somewhat. We confirm that commercial activities relocate following the introduction of congestion pricing and that the adjustment process is costly to merchants. [ABSTRACT FROM AUTHOR]

• Features of the road environment from Google Street View image reflecting the cyclist's perception of space. • Novel cycling safety level indicators that incorporate both collision and injury risks. • A tri-tiered cycling safety level prediction spanning the entirety of London's road network. • Associations between road environment features and cycling safety levels are explained using SHAP values. • The 'Second road effect' has a significant impact on road safety. Cycling, as a routine mode of travel, offers significant benefits in promoting health, eliminating emissions, and alleviating traffic congestion. Many cities, including London, have introduced various policies and measures to promote 'active travel' in view of its manifold advantages. Nevertheless, the reality is not as desirable as expected. Existing studies suggest that cyclists' perceptions of cycling safety significantly hinder the broader adoption of cycling. Our study investigates the perceived cycling safety and unpacks the association between the cycling safety level and the road environment, taking London as a case study. First, we proposed novel cycling safety level indicators that incorporate both collision and injury risks, based on which a tri-tiered cycling safety level prediction spanning the entirety of London's road network has been generated with good accuracy. Second, we assessed the road environment by harnessing imagery features of street view reflecting the cyclist's perception of space and combined it with road features of cycle accident sites. Finally, associations between road environment features and cycling safety levels have been explained using SHAP values, leading to tailored policy recommendations. Our research has identified several key factors that contribute to a risky environment for cycling. Among these, the "second road effects," which refers to roads intersecting with the road where the accident occurred, is the most critical to cycling safety levels. This would also support and further contribute to the literature on road safety. Other results related to road greenery, speed limits, etc, are also discussed in detail. In summary, our study offers insights into urban design and transport planning, emphasising the perceived cycling safety of road environment. [ABSTRACT FROM AUTHOR]

The rapid urban population growth introduces novel challenges to urban mobility scenarios, requiring innovative and connected solutions to relieve traffic congestion and enhance transportation efficiency, using unusual roads and public transportation modals. For instance, multi-modal routes allow more flexible modal combinations to offer smart multi-modal mobility. In this context, the route selection service must rely on different context information, such as criminality, accidents, air quality, and others, where Internet of Things technologies introduce active and ubiquitous sensing of many contexts along the urban scenario, providing data for statistical analysis to offer a safer and healthier urban reality. However, designing an efficient multi-modal route selection service that considers different context information to offer personalized routes is important. In this article, we describe a context-aware route selection service that considers adequate contextual information to provide routes according to the user's preference. The multi-modal route selection service applies a multi-criteria method to give different degrees of importance to each criterion based on the user profile (i.e., Worker, Green, Safe, and Tourist). We present extensive evaluation results from applying our multi-modal route selection approach to a London dataset. The approach successfully enables user-preferred transportation choices using four balanced selection profiles and ten route features. The proposed multi-modal route selection service demonstrates better performance in terms of economic, ecological, and time-saving metrics for each user profile compared to a greedy selection manner. [ABSTRACT FROM AUTHOR]

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future. [ABSTRACT FROM AUTHOR]

To mitigate carbon emissions and exploit underutilized lanes (e.g., bus lanes), a credit-based reservation scheme (CRS) is proposed to optimize road resources, allowing travelers to switch from the high-emission mode (e.g., driving in normal lanes) to the low-emission mode (e.g., taking buses) and gain carbon credits. These credits can then be used to access the reward mode (e.g., using priority lanes). This CRS design problem is formulated as a leader–follower game, where the transport authority determines the reward ratio based on the mode choices of travelers. We analyze how the CRS affects emissions and traffic congestion, and conduct two numerical studies on Beijing and London to verify our findings. Results demonstrate that when the reward ratio is set at a moderate level and the generalized cost of the reward mode is higher than the other two modes, the CRS can effectively reduce system-wide carbon emissions and traffic congestion. • Propose a credit-based reservation scheme using priority lanes to reduce emissions. • Build a leader–follower game upon travelers' long-term mode choice behaviors. • Use two case studies: London and Beijing to investigate the benefits of the scheme. • Proper scheme design can reduce system-wide carbon emissions and congestion. [ABSTRACT FROM AUTHOR]

A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires rapid accident investigation and management. In this paper, we aim to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report. Methodology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic accidents within a scenario-wise Wasserstein ambiguity set. To the best of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with the CVaR metric. We demonstrate that the proposed RSO model can be reformulated into a mixed-integer programming model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark models. The numerical results yield valuable insights for traffic supervisory agencies. • We propose a drone pre-positioning problem for traffic accident rapid assessment. • We propose an RSO model that incorporates the uncertain traffic accident. • We demonstrate that the RSO model admits a tractable MILP reformulation. • A real world instance is used to verify the practicality of our model. [ABSTRACT FROM AUTHOR]

The London congestion charging (LCC) scheme was first introduced in 2003. It did not only help alleviate traffic congestion and reduce vehicle emissions, but also had favorable safety effect. Western Extension of LCC was applied in 2007, but then removed in 2011. It was suggested that adjacent areas of the congestion charging zone could also be benefited. As well, the benefits would not disappear immediately after the removal of congestion charging scheme, which is known as residual effect. This paper attempts to examine the affected area and residual period of the safety benefits by the LCC scheme using the traffic and crash data from 352 Middle Super Output Areas (MSOAs) of London, with which the original LCC scheme was imposed in 24 MSOAs ('treatment' units for Analysis I), the Western Extension scheme was imposed in 27 MSOAs ('treatment' units for Analysis II), and no congestion charging was implemented at all in 301 MSOAs ('control' units for both Analysis I and II). Factors including traffic flow, land use, built environment and population demographics are considered. To eliminate the bias by the selection of treatment and control groups, Propensity Score matching (PSM) method is applied. Results indicate that favorable effect on safety is prevalent in the 1.5 km buffer area of LCC zone. On the other hand, for the residual effect, considerable crash reduction could be found in the first year after the removal of Western Extension of LCC. However, no evidence could be established for significant crash reduction in the second and third years after the removal. Findings should be indicative to the transport management policy that could improve the road safety in the Greater London in the long run. • This study examined the safety effects of congestion charging using Propensity Score Matching approach. • Affected area and residual period of the safety benefits are examined. • Safety benefit is revealed in the 1.5 km buffer area of congestion charging zone. • Crash reduction is observed one year after the policy removal. [ABSTRACT FROM AUTHOR]

The ever-growing population and dynamic of urban traffic in the city of Sofia, as well as the utterly unsatisfactory condition of public transport have resulted in traffic jams on the capital’s main streets and boulevards, exceeded limits of atmospheric and noise pollution, and multiple other negative social effects. The capital’s residents prefer to use their personal cars and believe that public transport requires significant improvements – a notion that has been confirmed by a survey of citizens’ opinions on the state and quality of urban passenger transport in the capital. All of this has led to a firm conviction in the necessity for adopting specific measures to improve the quality of urban passenger transport, along with determining the topicality of the current paper. The purpose of the article is to name specific measures for improving the social effectiveness of Sofia’s public transport, based on an analysis of its dynamic and state. [ABSTRACT FROM AUTHOR]

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research. [ABSTRACT FROM AUTHOR]

The dynamics by which policy ideas rise and fall can be linked to the intimate interactions between discrete physical time dimensions, such as policy windows, and distinctive perceptions of time, particularly the connections made between past, present and future in the form of narratives. Kingdon describes how ‘an idea whose time has come’ can be carried forward in the key policy change instrument of a policy window, but much remains to be understood about the dynamics of windows, including why they apparently open for a relatively short time period, and the significance of the legacy left by windows in terms of their ability to influence later events. The article seeks a greater understanding of these dynamics through an examination of the potentially vital role played by the construction of narratives. The case study is taken from the politically sensitive area of urban road pricing, in the form of the London Congestion Charge. [ABSTRACT FROM PUBLISHER]

Highlights: [•] New approaches to visualise evolution of traffic congestions in large size cities such as London. [•] Enable identifying dispersion and dissipation of congestion on the entire road network. [•] Allow seeing dispersion and dissipation of congestion at the link level. [ABSTRACT FROM AUTHOR]

Developing a network for cycle traffic is a complex process, particularly in a dense and heavily motor trafficked city such as London. The London Cycle Network Plus has delivered cycle routes on strategic highways, and local cycling stakeholders have assisted in the design process through cycle route implementation and stakeholder plans. Stakeholders were trained in aspects of highway engineering and used the London cycling design standards as a reference tool. The paper summarises the lessons learnt, and concludes that knowledge from stakeholders is needed to help in the design process, and such intense involvement was novel in transport planning. The current proposals for cycle superhighways and other borough transport schemes seek to build on previous participative successes and also emphasise planning issues in the geographical neighbourhood of routes. [ABSTRACT FROM AUTHOR]

The article presents tips to travelers about getting around London in England. They can travel through public transportation via the London Underground and the city's buses which operate on the same system of six fare zones. In taking taxis, travelers can head for a cab rank or hail one in the street or take minicars when the regular ones are scarce or the Tube stops operating. They are discouraged to drive around the city due to traffic and parking. The city has designated paths for bicycles along the streets.