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• A public transit accessibility under heat exposure model is studied with time-dependent consideration. • A column generation method to urban rail-bus-walk service network integration is devised. • Experiments in Beijing show the impact of heat exposure on public transit accessibility. The evolution of urban transportation systems is increasingly driven by the integration of rail, bus, and walking pathways, forming a cohesive service network. This urban rail-bus-walk service network integration is pivotal in addressing urban challenges and ensuring seamless mobility. Nevertheless, the effectiveness of such integrated systems depends on their accessibility and the willingness of individuals to take them as their primary transportation mode. In the context of climate change, extreme heat events pose an increasing threat to passenger comfort and health, which in turn, affects individuals' decisions to use public transit. Additionally, walking to transit stops or waiting for transportation becomes more challenging during such extreme heat conditions. This paper tackles the challenge of comprehensively addressing the evolving issues related to public transit accessibility in response to rising temperatures, an area of research that has received limited attention until now. This paper introduced an approach aimed at optimizing the urban rail-bus-walk service network with a focus on both accessibility and heat exposure considerations. By calculating the transportation accessibility level under varying temperature conditions and identifying its vulnerability to high temperatures, this paper presents a methodology to enhance the transit service. Such improvement involves not only adjustments to the integration network but also considering of cooling facilities, such as shaded routes and shelters. Column generation is applied to solve this problem and the optimization result demonstrates improvement of accessibility under the extreme heat condition. [ABSTRACT FROM AUTHOR]

The transition to alternative energy sources and the adoption of on-demand operating modes in urban bus systems are crucial steps towards reducing carbon footprints and improving public transit services. This paper presents a two-phase approach for the collaborative optimization of charging schedules and passenger services, aimed at enhancing the operation of on-demand electric bus systems. First, we propose a label-setting dynamic programming algorithm that enables the efficient generation of bus-trips for each bus line in response to passenger requests. Second, we introduce a time–space network optimization model that facilitates integrated multiple bus-trip planning for the transit network, involving multiple bus lines and charging spots. The model selects bus-trips from various time–space arcs, which represent passenger carrying, bus deployment, and bus charging activities. To validate the effectiveness of our approach, we conduct a case study using real-world data from bus lines in Beijing, China. Computational results demonstrate that our approach can handle on-demand electric bus operations within minutes of solution time, efficiently serving over 2,000 passengers. Practically, our approach achieves a notable reduction in average transit time and effectively reduces the waste of public transit resources. The proposed approach can serve as a beneficial tool for decision-makers and operators seeking to enhance the performance and environmental impact of their electric bus systems. • This paper aims at enhancing on-demand bus operational efficiency. • This paper proposes an optimization approach for electric bus operations. • This paper provides practical solutions for responsive transit systems. [ABSTRACT FROM AUTHOR]

• Accurate risk path forecasting in rail transit systems is a significant and challenging task. • A probabilistic deep learning framework that can process multisource data for risk path forecasting. • The model can effectively handle safety issues such as cascade failures. Failure-related urban rail events can disrupt transit system operations and traffic flow and lead to serious safety problems worldwide. A domino effect can occur if potential cascading events of major failures are not effectively mitigated and controlled. Therefore, accurate risk path forecasting in rail transit systems is a significant and challenging task. Because of the limitations of traditional models in terms of computational power and feature extraction capabilities, this paper proposes a probabilistic deep learning framework that can process multisource data for risk path forecasting in urban rail transit. This paper first proposes a method for constructing a large-scale risk path ground truth dataset when fault events occur. Then, the framework uses a graph-based feature mapping method to model social media, passenger flow, and station failure information. Finally, we proposed a spatiotemporal feature extractor and a dynamic difference weighting loss function to extract features and optimize parameters. We apply real-world data from 2018 to 2019 from the Beijing urban rail transit system for experimental analysis. The analyzed results demonstrate that the proposed model exceeds the baseline models by at least 2.9% in terms of F 1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F 1 value and the proposed attention mechanism and dynamic loss function weights can effectively improve the forecasting performance of the model. Furthermore, the results of the different steps ahead demonstrate that the above results are robust. The application of the model can effectively handle safety issues such as cascade failures. [ABSTRACT FROM AUTHOR]

Accurately predicting passengers' origin and destination (OD) demand and analyzing their spatiotemporal characteristics are the key to efficient operation and management of urban rail transit. While obtaining these spatiotemporal characteristics and thus making a short-term OD prediction are a big challenge for a model due to its high dimensionality and uncertainty. This paper proposes a pattern match algorithm based on t -distribution stochastic neighbor embedding and K nearest neighbors (TSNE-KNN) to promote the prediction performance and introduces similarity indicators to explore these features of OD flow and their relationship with the forecasting performance. Analysis of automatic fare collection data of Beijing rail transit shows that the TSNE-KNN model is superior to other state-of-the-art approaches, even including deep neural network models, and the similarity, which is affected by the functional attributes of station, the surrounding land use attributes and the degree of development of the road network, can be a universal indicator to indirectly reflect the time–space properties of the OD flow. It is found that as the similarity of daily OD flows decreases, the performance of short-term OD prediction of decreases, and rail transit stations are gradually shifting from the periphery to the center of the city. [ABSTRACT FROM AUTHOR]

Integrating emerging shared mobility with traditional fixed-line public transport is a promising solution to the mismatch between supply and demand in urban transportation systems. The advent of modular vehicles (MVs) provides opportunities for more flexible and seamless intermodal transit. The MVs, which have been implemented, are comprised of automated modular units (MUs), and can dynamically change the number of MUs comprising them at different times and stops. However, this innovative intermodal urban transit brings with it a new level of dynamism and uncertainty. In this paper, we study the problem of jointly optimizing the timetable and the vehicle schedule within an intermodal urban transit network utilizing MVs within the context of distributionally robust optimization (DRO), which allows MVs to dynamically (de)couple at each stop and permits flexible circulations of MUs across different transportation modes. We propose a DRO formulation to explore the trade-off between operators and passengers, with the objective of minimizing the worst-case expectation of the weighted sum of passengers' and operating costs. Furthermore, to address the computational intractability of the proposed DRO model, we design a discrepancy-based ambiguity set to reformulate it into a mixed-integer linear programming model. In order to obtain high-quality solutionss of realistic instances, we develop a customized decomposition-based algorithm. Extensive numerical experiments demonstrate the effectiveness of the proposed approach. The computational results of real-world case studies based on the operational data of Beijing Bus Line illustrate that the proposed integrated timetabling and vehicle scheduling method reduces the expected value of passengers' and operating costs by about 6% in comparison with the practical timetable and fixed-capacity vehicles typically used in the Beijing bus system. • We address the timetabling and vehicle scheduling problem concerning modular units. • We formulate a distributionally robust model for the investigated problem. • We develop an algorithm combining ALNS and GUROBI to generate high-quality solutions. • We verify the effectiveness of our proposed approaches by numerical experiments. [ABSTRACT FROM AUTHOR]

• Deal with the unfairness problem of passenger waiting times in an oversaturated URT line from the point of view of the individual time-dependent passenger demand through timetabling optimization • Develop an approximate general model with a min-max type objective function to formulate the travel schemes of passengers and train timetabling (including the stop patterns) by clustering passengers into groups, where the problem scale is controlled by the defined aggregation granularity parameter • Design a preprocessing method to reduce the space of feasible solutions for small-scale cases and a solving algorithm based on variable neighborhood search for large-scale cases • To justify the choice of our min-max type objective function, a computational comparison between our model and the efficiency-oriented model was made. The performance of our VNS and the impact of the aggregation granularity parameter were investigated for different scales in both small instances and a real case study In oversaturated urban rail transit systems, passengers departing from downstream stations often experience long waiting times due to unbalanced space-time demand and limited transit capacity. This is often prevalent during morning and evening peak periods in transit systems. This paper aims to mitigate the unfairness of waiting time among a time-varying number of passengers through train timetable's adjustment by optimizing the train skip-stopping pattern. We develop an approximate general model by clustering passengers into groups and introducing an aggregation granularity parameter. To characterize feasible passenger travel patterns, both rigid first-in-first-out rule and capacity constraints are incorporated in the proposed model. Preprocessing is proposed to reduce the space of solutions. Some small-scale case studies show that the proposed method outperforms the original timetable and the preprocessing is effective to reduce computation time. Case studies based on the Batong line of Beijing rail transit network are conducted, in which a variable neighborhood search algorithm is applied to obtain high-quality solutions in short computing times. The results show that the proposed approach not only mitigates the unfairness of waiting time among passengers but also improves other efficiency evaluation indexes, including the average waiting time and the maximum number of missed trains. We also investigate the impact of the aggregation granularity parameter on the computational effort and solution accuracy. [ABSTRACT FROM AUTHOR]

Across the world, Transit Oriented Development (TOD) offers a strategy to integrate land use and transport systems by clustering urban developments around public transport nodes in functionally dense and diverse, pedestrian- and cycling-friendly areas. Even though the basic philosophy of TOD seems to be the same in all contexts, its specific applications greatly differ in form, function and impacts, calling for context-based TOD typologies that can help map these local specificities and better focus policy interventions. In recent years, TOD has also been widely advocated and applied in China; however, so far no study has systematically developed a TOD typology in a Chinese context. This paper fills this gap for the case of the Beijing metropolitan area. The approach is based on the node-place model, introduced by Bertolini (1996, 1999) to chart ‘Transit’ and ‘Development’ components, expanding it with a third, ‘Oriented’, dimension to quantify the degree of orientation of transit and development components towards each other. The paper reviewed the main TOD indicators in the international literature, selected those appropriate for the Beijing context, and classified the metro station areas into TOD types through a cluster analysis. The six identified types of metro station areas in Beijing demonstrate how the context-specific typology can support local urban and transport planners, designers and policymakers when considering future interventions. [ABSTRACT FROM AUTHOR]

To calculate the public transit accessibility value and its differences in a particular area to surrounding regions, this paper raised a new concept-- Area Public Transit Accessibility (APTA). This concept is based on the passenger travel behavior, travel psychology hypothesis and the service range of transit network and road network. By using the software –ArcGIS, this paper established the formula and showed the calculation steps. This method was applied to evaluate the accessibility values of every traffic zone in Beijing Chaoyang district. The results showed that the APTA can give us a clear description of the accessibility value level in a particular area to surrounding regions, which provides quantitative information for location analysis and public transit network optimization [ABSTRACT FROM AUTHOR]

Despite massive new town development in megacities in China, none of the prior studies examined commuting disparities of residents of different income groups living in various types of new towns. Based on established literature on urban structure and commuting and income disparities in commuting, this paper has examined the roles of income and new town types in commuting distance, mode, and time differences. Based on Beijing's new town development context, we categorize its six major new towns into residential, national park-driven industrial, market-based industrial, and satellite new towns. Using descriptive analysis and regressions on 595 new town residents, we find that high-income commuters tend to have longer commuting distances and times and are more likely to drive to workplaces. Those living in the residential new town tend to have longer commuting distances and times. Results also show the transportation resource differences in different types of new towns. Those living in the residential new town are more likely to commute by public transit. Results also show the transportation difficulties for low-income residents who live in satellite towns. Though high-income commuters living in those towns have significantly longer commuting distances, they do not necessarily have longer commuting times than low-income people living in these towns. This study has several implications for future research and policy implications for new town development in China and other transit-dependent cities in Asia and Global South. • This paper examines commuting disparities across different types of new towns and income groups in Beijing. • High-income commuters tend to have longer commuting distances and times and are more likely to drive. • Those living in the residential new town tend to have longer commuting distances and times. • Those living in the residential new town are more likely to commute by public transit. • Results also show the transportation difficulties for low-income commuters living in satellite towns. [ABSTRACT FROM AUTHOR]

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period. [ABSTRACT FROM AUTHOR]

Research has shown, that the replacement of conventional vehicles (CVs) by electric vehicles (EVs) can reduce urban heat island intensity (UHII). This leads not only to a more comfortable climate during summer but also to a reduction of carbon dioxide emissions from the vehicles themselves and to a reduction in the need for air conditioning (AC) and associated emissions. The current study examines to what extent a range of different mobility concepts, can lead to a reduction in urban heat island intensity and carbon dioxide emissions in the city of Beijing, China. For this purpose, the impact that electric vehicles fuelled with the average energy mix, hydrogen vehicles (HyVs) fuelled with hydrogen from electrolysis from the regular energy mix, HyVs fuelled with hydrogen produced through wind power, energy efficient CVs, EVs fuelled with wind power and energy efficient metro light railway (metro) travel have on summer heat island intensity (SHII) and CO 2 -emissions is analysed. The lowest SHII mitigation effect was found for a replacement of CVs with fuel efficient diesel cars while hydrogen cars that are fuelled with hydrogen generated from the average electricity mix even leads to higher SHII and higher CO 2 emissions. The largest SHII mitigation effect was found for a scenario where conventional vehicles are replaced with energy efficient metro travel. The CO 2 saving potential is highest for EVs which are powered with electricity generated from wind turbines, closely followed by HyVs that are fuelled with hydrogen generated from electrolysis of water using wind electricity. The paper helps policymakers and consumers to make the right choices when it comes to environmentally friendly mobility options. • Summer heat island intensity (SHII) is mitigated through different mobility choices. • CO 2 reduction by over 12,000 tons/day is possible through reduced usage of air conditioning. • Energy efficient public transportation leads to the highest mitigation of SHII and CO 2 levels. • Electric and hydrogen cars fuelled with renewable energies mitigate SHII and CO 2 levels. • Hydrogen cars fuelled with non-renewable energies lead to higher SHII and CO 2 levels. [ABSTRACT FROM AUTHOR]

This paper studies the impacts of congestion pricing and reward strategies on automobile travelers' morning commute mode shift decisions using stated preference travel mode choice data of over 1000 automobile travelers collected in Beijing inner districts. To address the complex impacts of these strategies on automobile travelers, a stage-based model framework is developed to analyze their mode shift decision-making process (whether they will shift from using automobile to public transit, biking or walking or continue using automobile) under these strategies. Four multilevel structural equation models are created for participants using automobile (personal vehicle and/or taxi) as the most common mode of transportation (hereafter referred to as "more habitual automobile travelers") and those using automobile as second most common mode (hereafter referred to as "less habitual automobile travelers") under each strategy. Model estimation results show that the impacts of latent psychological factors on mode shift decisions under congestion pricing and reward strategies are significantly different between more and less habitual automobile travelers. The results also show that congestion pricing strategies are more effective than reward strategies in promoting mode shifts among more habitual automobile travelers, while the opposite is observed for less habitual automobile travelers. This study provides insights for designing congestion pricing and reward strategies and illustrates the importance of developing complementary modules that target numerous factors in different stages of the mode shift decision-making process to effectively promote mode shifts from automobile to sustainable travel modes in China. [ABSTRACT FROM AUTHOR]

Highlights • The relationship between commuting behaviour and congestion satisfaction are significant. • Semi-parameter model results are robust. • Heterogeneous effects on perceived satisfaction about congestion across social-spatial gradients. Abstract Megacity growth in developing countries suffers from heavy congestion, which undermines satisfaction as experienced by local residents. Drawing on a large scale individual survey in Beijing, this paper explores the social-spatial differentiation of urbanites' satisfaction perceptions about congestion by comparing residents with various motorized commuting modes including car, shuttle bus, and public transit. The results show that shuttle bus commuters are significantly associated with better satisfaction perceptions about congestion than other commuting mode users. We find that perceived wellbeing implications vary with demographic characteristics and transport accessibility characteristics. Findings from this study suggest the importance of considering neighborhood built environment into the design of land use and transport policy aiming to enhance commuters' subjective experiences towards congestion. [ABSTRACT FROM AUTHOR]

Urban rail transit has been commonly regarded as an efficient transportation mode for alleviating the negative impacts of rapid urbanization. However, the complex and comprehensive built environment caused by rapid urbanization prevents policymakers and planners from understanding the performance of urban rail transit, particularly around transit stations. Developing station area typologies plays an important role in describing the characteristics of station areas. Numerous studies have developed typologies of station areas over the past decades; however, few have attempted to estimate the distribution of station types per station. To fill this gap, this paper first proposed the importance of a land use type (ILUT) index based on the term frequency-inverse document frequency method. This method avoids mutual concealment among land use types, to extract key built environment features based on built area datasets of the buffer areas of 337 selected urban rail stations in Beijing. Subsequently, the ILUT index was used to estimate the distribution of the station types per station using the latent Dirichlet allocation model. The 12 identified types of urban rail station areas in Beijing demonstrated that land use types consist of station types and presented the distribution of the station types with their probabilities, revealing the current performance of station areas. This paper proposes a description approach that will help policymakers and planners to effectively and efficiently identify and analyze built environment features. • Probability-based typology approach is proposed to describe built environments. • Index for extracting relatively important built environment features is developed. • Twelve types of station areas are identified using built area datasets in Beijing. • Distribution of 12 station types per station is estimated and visualized. [ABSTRACT FROM AUTHOR]

• Construct a unified model for integrating timetable, bus formation and vehicle scheduling problems. • Schedule autonomous modular vehicles (AMVs) in both directions simultaneously. • Design a decomposition-based iterative solution framework based on Alternating Direction Method of Multipliers (ADMM) algorithm to solve the origin model efficiently. • Solve the subproblem efficiently with a customized time-dependent shortest path algorithm. • Introduce AMVs into public transport systems can reduce overall system costs and increase vehicle utilization. This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capacity). To increase vehicle utilization, a flexible scheduling strategy is proposed that allows AMVs to detach from one modular bus and join another modular bus in either direction of a bidirectional line. In particular, the penalty cost for each detachment or joining operation, as well as the limited number of available AMVs is explicitly considered. We formulate a unified model for the integrated optimization of the modular bus service (timetable and bus formation) and vehicle scheduling by introducing two types of decision variables. The objective is to minimize overall system costs, including passenger waiting time cost, operational costs, and detachment/joining penalty costs. The two types of decision variables are coupled by a vehicle resource consistency constraint, ensuring the conformity of the modular bus service and vehicle scheduling decisions. To tackle the complexity of our model, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose it into two subproblems, which can be efficiently solved using a customized forward dynamic programming algorithm and a commercial solver. The model is validated using illustrative examples and a real-world instance from the Beijing Public Transport system, and it is compared with two benchmark models. Our results demonstrate the efficiency of the ADMM-based solution framework for solving the integrated optimization model. Furthermore, our findings indicate that the use of AMVs in PT systems can lead to reduced overall system costs and increased vehicle utilization. [ABSTRACT FROM AUTHOR]

It is widely believed air pollution is an obstacle to cycling as it has negative effects on cyclists’ health outcomes and deteriorates their cycling experiences. However, the empirical studies investigating the impact of air pollution on cycling behaviour remains scarce. The aim of this paper is to fill the gap by looking at Beijing as a case study. The authors conducted a survey of 307 cyclists on the days with different levels of air quality in terms of concentration of PM 2.5 in 2015. The results show that in the polluted weather, those who persist in cycling are more likely to be male, over 30 years old, lower income or those who travel short distances. Specifically, female cyclists have a higher tendency to shift from cycling to public transit than the males and medium and high-income earners are more likely to shift to using a car than low income earners. The residents’ subjective perceptions of safety and comfort have major effects on their cycling behaviour. A higher perception of comfort and safety is related to a higher possibility of continuing cycling when air quality became polluted. Cycling for commuting trips is less likely to be replaced by other modes than cycling for non-commuting trips, such as shopping. Results of this study reveal that improving air quality in a metropolitan area such as Beijing has co-benefits of cycling renaissance. The huge investments into cycling infrastructure should be integrated with policies designed to create an attractive environment for cycling. [ABSTRACT FROM AUTHOR]

Rail transit network (RTN) has become the core of urban public transport system. Because RTN’s topological structure has become complicated with its scale, the phenomenon of overload operation frequently appears at stations or lines, reducing the stability of RTN in operation. There is a need to identify the key stations and to analyze the vulnerability of RTN in depth. This paper quantitatively analyzes statistical topology parameters of Beijing rail transit network (BRTN) based on complex network theory (CNT). The key stations are evaluated by different evaluation indicators, such as node degree, betweenness, and strength. Then, a model is proposed to analyze the cascading failures of weighted BRTN considering loading and redistribution of multi-static passenger flow based on coupled map lattices (CML). Results show that when the external perturbation is larger, the time of all stations failure is earlier once the failure is triggered. Moreover, the perturbation threshold for the cascading failure of weighted BRTN is determined. Transfer regularities of the risk stations in BRTN are researched under different combinations of the topological coupling coefficients and the flow coupling coefficients. Under attack, the threshold of loop line damaged is smaller than a straight line. And, while attacking the loop line, the discrete degree of peak time and all station failure time are higher under different perturbations, meaning that failure is hard to control. This study provides recommendations on developing strategies for RTN design and performance evaluation. Future studies will focus on the coordination of weighted complex public transit networks other than passenger flow redistribution within RTN. [ABSTRACT FROM AUTHOR]

User profiles, considered as one of the fundamental inputs of recommendation systems and customized services, can be rationally applied in the public transport domain to represent passengers' characteristics and behavioral preferences. A user profile of a bus passenger, termed as a bus passenger profile (BPP), is an assortment of labels containing passengers' travel features. This paper proposes a data-driven framework for developing BPP so as to provide guidance on how to create and estimate user profiles for bus passengers based on smart card data. The proposed method comprises three steps. (i) Data preprocessing aimed at extracting key information and preparing passenger profiling. (ii) A tag system aimed at storing the estimated travel features of passengers. (iii) Knowledge graphs aimed at connecting various BPP with semantic edges for practical application of prior knowledge in downstream tasks. The developed framework is implemented in a case study of the Beijing bus system. Deployment of the developed framework has demonstrated that it can satisfactorily develop BPP, while prior knowledge from the BPP-based knowledge graphs can benefit downstream tasks. [ABSTRACT FROM AUTHOR]

Public transport services are believed to play an important role in constraining car use. However, it has been found that cars are still used at high rates near many metro stations. This paper aims to explore car ownership and car use in people living close to metro stations. Taking Beijing, China, as a case study, this study shows that the likelihood of owning a car, and the number of work and non-work vehicle kilometers traveled (VKT), is lower in areas with higher mixed land use. Residents’ income and family structure both affect VKT. People with an awareness of sustainable land use and transportation modes use fewer VKT. The study also has some interesting new findings in the Chinese context. It found that residential density was not significantly related to VKT. Interestingly, neighborhood type was firmly associated with car ownership and use: those who live in Danwei neighborhoods generated more work VKT, and those living in indemnificatory neighborhoods used more non-work VKT. Additionally, renters were less likely to own cars than house owners, and people with Beijing hukou generated more VKT. People preferring to live in close proximity to the metro system and travel by foot were more likely to own cars in the future than other people. The results suggest that increasing land use diversity and the accessibility of living amenities in neighborhoods near metro stations could enhance the performance of transport systems by reducing dependence on cars. Housing and transport policies, along with behavior-education programs, could strengthen this effect. [ABSTRACT FROM AUTHOR]

Developing countries like China have experienced substantial city transformations over the past decade. City transformations are characterized by transportation innovations that allow individuals to access to speedy commuting modes for work activities and offer potential influences on commuting behavior. This paper examines the potential effects of subway system expansion in Beijing on commuting behavior. Our methodological design controls for spatial effects by employing Bayesian multilevel binary logistic models with spatial random effects. Using cross-sectional individual surveys in Beijing, the results suggest that there is a significant rise in subway commuting trips while non-motorized and bus commuting trips are reduced with the new subway expansion. Model comparison results show evidence about the presence of spatial effects in influencing the role of built environment characteristics to play in the commuting behavior analysis. [ABSTRACT FROM AUTHOR]

The emergence of dockless bike-sharing (DBS) has made it more convenient for residents to travel within the city. The integration of public transit and DBS provides an effective method to sustainable transportation development. While some studies have accounted for heterogeneity of travel behavior, very few have considered how attitudes and preferences towards DBS affect combined travel mode choice. This study aims to reveal the effect of residents' attitude towards bike-sharing on combined travel mode choice using stated-preference data collected in Beijing, China. An integrated model of choice model and latent variables based on nested logit model (ICLV-NL) is used to incorporate two latent variables of social benefit and satisfaction with bike-sharing into the discrete choice model and thereby captures relationships between attitudinal factors and observed variables. The results indicate that compared with multinomial logit and nested logit models, the ICLV-NL model yields a better fit and explanatory power. The ICLV-NL model results show that positive impressions on bike-sharing make residents more likely to choose the integration of public transit and DBS, and the pick-up distance of DBS significantly affects the use of DBS as a feeder mode to public transit. Surprisingly, the status of DBS does not have a significant impact on travel mode choice. This paper provides more insights into the choice of the integration of DBS and public transit, which is helpful to the development of green and sustainable transportation. • An ICLV-NL model is proposed to account for the unobservable attitude factors towards DBS. • Users' attitudes, including social benefits and satisfaction with DBS, positively affect the choice of integration mode. • Female, high-education, low-income, and young passengers are more likely to utilize the integration of DBS and public transit. [ABSTRACT FROM AUTHOR]

The subway and bus together contribute to the structure of public transport network in a metropolis. However, integrated service of public transport is not satisfied due to the lower timetable synchronization between these two modes at transfer stations. In this paper, a double-layer public transport network composed of subway/bus lines and stations is first created. Then, two stages are included in the timetable optimization process. In the first stage, a user equilibrium assignment model and a capacity restriction iterative algorithm are employed to obtain the passenger distribution in the subway–bus public transport transfer network. Based on this distribution, with the consideration of the synchronization and section service level, a mixed-integer linear programming (MILP) model is applied to optimize the timetables of the subway and bus networks in the second stage. Computational results using real Automatic Fare Collection System (AFC) data from the public transport system in Beijing are reported, and these results show that the suggested model is efficient in improving passenger service levels. • Two stages (passenger assignment and timetabling) are in the optimization. • A double-layer network is constructed to describe subway/bus lines and stations. • A user equilibrium is for passengers distribution in the double-layer network. • Four types of space-time relationship of synchronization are considered. • Synchronization and section service level are included in the model objectives. [ABSTRACT FROM AUTHOR]

In recent years, customized bus (CB) service has become an innovative model for public transportation in China. Its ability to provide advanced, timely, personalized, and flexible service has made it very popular. However, previous studies have ignored its diversion effect: How can CB service help conventional buses reduce on-board crowding during rush hours? In this paper, we optimize conventional and CB buses as a whole based on an empirical study in Beijing. We propose a new model to compare the cost of single conventional buses (Single Con) and the integration of conventional and customized buses (Con+ CB). Passenger diversions from conventional buses to CB ones are specifically considered. To do this we explicitly measure the on-board crowding cost of conventional buses by load factor and distance factor. Real-world data from Beijing, China, are employed to assess the model. We obtained the departure arrangements and the corresponding costs under different demand cases. The results are analyzed and compared. Sensitivity analyses are then conducted to study the effects of line-haul distance and load factor on cost. The results show that (1) adding new modes of transportation does not always significantly reduce the total cost of bus service; (2) CB buses can effectively ease congestion on conventional buses only at high demand densities but not at low densities; (3) crowding cost increases caused by the distance factor far exceed those caused by the load factor. • Explicitly measure crowding cost of conventional buses. • Propose cost models of Single Con buses and integration of Con+ CB buses. • Employ the real-world data in Beijing to assess the model. [ABSTRACT FROM AUTHOR]

This paper seeks to understand extreme public transit riders in Beijing using both traditional household surveys and emerging new data sources such as Smart Card Data (SCD). We focus on four types of extreme transit behaviors: public transit riders who (1) travel significantly earlier than average riders (‘early birds’); (2) ride in unusual late hours (‘night owls’); (3) commute in excessively long distance (‘tireless itinerants’); and (4) make significantly more trips per day (‘recurring itinerants’). SCD are used to identify the spatiotemporal patterns of these four extreme transit behaviors. In addition, household surveys are employed to supplement the socioeconomic background and tentatively profile extreme travelers. While the research findings are useful to guide urban governance and planning in Beijing, our methodology and procedures can be extended to understand travel patterns elsewhere. [ABSTRACT FROM AUTHOR]

Aiming to alleviate traffic jams, many traffic management strategies/policies are adopted to nudge travelers to re-arrange their departure time or switch from driving to public transit or non-motorized mode. Analytical travel behavior model is needed to predict travelers’ departure time choice and mode switch under such strategies. In this paper, we developed an agent-based model for travellers’ choices of mode and departure time. Departing from the traditional utility maximization theory, this model focuses on the decision-making process based on imperfect information, bounded and distinctive rationalities. In the modeling framework, travelers accumulate experiences and update their spatial and temporal knowledge through a Bayesian learning process. Before making a trip, travelers decide whether to search for alternative departure time and/or travel mode according to their expected search gain and cost. When an additional search happens, travelers decide whether or not to switch to the new departure time and travel mode according to a series of decision conditions. Both the search and decision processes are represented by production (if–then) rules derived from a joint revealed/stated-preference survey data collected in Beijing. Then the agent-based model is applied to evaluate congestion charge policies with various demand scenarios in the 2nd ring road of Beijing. Results suggest that the model can display the peak spreading and mode switch process practically. [ABSTRACT FROM AUTHOR]

• Developed a macro framework optimized transportation network utility. • Proposed a general passenger utility maximization model considered efficiency and proportional fairness. • Proved the existence of optimal passenger allocation with maximum passenger utility. • Jointly optimize passenger allocation and vehicle schedules by maximizing transportation network utility. • Numerical tests verified the improvement of passengers' fairness and operation's cost. To deal with the contradiction between oversaturated demand and the restricted transport infrastructure and capacity during peak periods in a rail transit system, several passenger flow control methods that include soft pricing incentive, rigorous physical barrier and so on are widely used in current scenarios. However, the traffic disorderliness at the bottleneck and the control unfairness for the flows on specific stations may occur with those methods in the rail transit corridor. To alleviate those problems, this paper focus on the passenger allocation and matching with the corresponding schedules by considering the fairness of passengers and cost of operation from a macro perspective, where the passenger trip plan can be collected via a reservation platform in advance. A passenger utility maximization model that programs passenger allocation is proposed firstly, and we theoretically prove that there is an optimal traffic flow allocation with maximum utility under the oversaturated flow condition for any network. The model is solved by the Newton iteration algorithm and further proven to effectively make a tradeoff between efficiency and fairness. Furthermore, the transportation network utility maximization model is constructed by jointly optimizing the passenger allocation and vehicle schedules, thereby saving the cost of vehicle operation while guaranteeing the fairness of passenger allocation macroscopically. The model is solved by Multi-tree depth-first search algorithm based on Lagrangian relaxation with a subgradient in an iterative manner. A real-world case from the Batong subway line in Beijing is studied to validate the proposed models and algorithms. The results show that the model can jointly optimize the passenger allocation and vehicle schedules effectively in both an economical and fair manner, and the passengers can pass through the bottleneck in an orderly way. [ABSTRACT FROM AUTHOR]

• The reliability and economy of UMES integrated with PTH are assessed. • Modeling of PTH flexibility considering both energy- and service-related constraints. • A system operation model combining energy management, vehicle dispatch and V2G is proposed. • A holistic evaluation framework embedded with multi-modal operation simulation is developed. • Influential factors for the contribution of PTH flexibility are analyzed. Growing penetration of renewable energy sources (RES) and emerging electrified loads (EEL) are bringing about increased difficulties for the power balancing and efficient operation of energy system, due to the impact of remarkable volatilities introduced. Public transport hub (PTH), as a new-style infrastructure of traffic service carriers, is regarded to offer a cogent solution to this problem, in terms of their potential operational flexibilities permitted by energy regulation, vehicular dispatch, and vehicle-to-grid (V2G) programs. As such, this paper carries out a comprehensive study to investigate the implication of harnessing PTH-enabled flexibility in a context of urban multi-energy system (UMES). The proposed methodology is established on a holistic reliability/economic analysis framework which is designed to indicate how UMES's performances would vary with different utilization of PTH resources. In order to portray the real-time controllability of PTH during operation, a PTH model that takes into account the impacts of both energy- and service-related aspects has been developed, with particular focus on the interdependencies between the energy and transportation sector. The operational simulation of UMES in presence of PTHs is implemented by using a multi-modal-based optimization model, which captures the effects of PTH flexibility under both normal and contingency scenarios integratedly. By embedding the above formulation into a sequential Monte Carlo simulation-based assessment framework, the contribution of PTH to the reliability and economy of UMES can be determined. Numerical studies are conducted based on an illustrative electricity-gas-heat test case and the real PTH datasets in Beijing. The simulation results confirm the significance of PTH-enabled flexibility in improving the performances of UMES. Also, it is demonstrated that the reserving strategy adopted, the composition of vehicle model, and the travel demand profile of passengers are the noteworthy factors that influence the profitability of PTH exploitation. [ABSTRACT FROM AUTHOR]

Sustainable mobility is critical to achieve sustainable development goals. Public policy makers worldwide have introduced different policy instruments to motivate sustainable mobility such as promoting car sharing and introducing car license plate lottery. Limited attention has been devoted to studying the effects of these types of policies on consumers' accessibility in urban mobility system, in particular when these policies are intended to achieve the same objective of promoting sustainable mobility. To fill this gap, based on the consumer discrete choice behavioral theory, this paper designs a stated choice experiment and performs discrete choice analysis to investigate how the interactions of these two policies influence consumers' mobility preferences and consumer surplus-based accessibility. By collecting data in Beijing, as an empirical context, we identify a nested logit model with a hierarchical choice structure, where bus and underground fall in the nest of public transit , while car sharing, private car and taxi are independent alternatives. Consumers who are participating in the car license plate lottery are more likely to choose car sharing, and car sharing can largely compensate for the loss of accessibility caused by the car license plate lottery policy, which implies that car sharing and the car license plate lottery policy are indeed complementary policies. However, the introduction of car sharing can also lead to an unintended consequence of higher total car usage by attracting more public transit users who never intended to buy or drive cars. This study contributes to the literature by proposing and implementing a generic theoretical framework for analyzing consumer preference-based accessibility for developing a sustainable mobility system and provides important practical implications. [ABSTRACT FROM AUTHOR]

In urban transportation systems, taxis are regarded as flexible, convenient, and time-saving. Taxi demand is affected by various built-environment factors and by the time of the day. Although many studies have investigated correlations between taxi demand and the built environment, the direct and spillover effects of built environment factors on taxi demand have not been examined at a fine spatial scale. To address this gap in the literature, this paper employs spatial econometric models using GPS-tracked taxi trips, mobile signaling data, and points of interest (POIs) to study taxi demand in Beijing at a 1-kilometer square grid resolution. The results show that, in the morning and evening peak hours, road network density has the strongest (positive) direct and indirect impact on taxi ridership. A relationship is also found between public transportation and taxi ridership: bus coverage has positive direct effects and insignificant indirect effects on taxi pick-ups and drop-offs, while subway coverage has negative indirect effects, suggesting that it may absorb taxi demand from surrounding grids. Results also indicate that various built-environment factors affect taxi demand differently at morning and evening peak times. This study reveals the complex nature of taxi ridership and has important implications for policymakers, transport planners, and other stakeholders in megacities around the world. • Use spatial autoregressive models to investigate the relation between built environment, socioeconomics and taxi ridership. • Conduct the analysis for morning and evening peak hours. • Built environment characteristics and socioeconomics influence taxi ridership via both local and spillover effects. • Offer a framework to incorporate various types of big data, including taxi data, mobile signaling data, points of interest. [ABSTRACT FROM AUTHOR]

• Trips by dockless bike-sharing show the highest travel satisfaction. • The longer assess time is related to lower satisfaction with dockless bike-sharing. • Travelers have higher satisfaction with dockless bike-sharing as the primary mode. • Pro-public transit attitude relates to higher satisfaction of dockless bike-sharing. • Residential road network has no effect on satisfaction with dockless bike-sharinga. Using survey data collected among residents in Beijing, this paper presents an investigation of the difference in travel satisfaction between dockless bike-sharing and other travel modes. The effects of individual, spatial and trip attributes on travel satisfaction with dockless bike-sharing are also identified. Our analysis adds to the empirical support for higher satisfaction with trips by dockless bike-sharing than trips by private bicycles. The assess time for shared bicycles is negatively associated with travel satisfaction with dockless bike-sharing. In addition, travelers have higher satisfaction with dockless bike-sharing when used as the primary mode than as the first-mile/last-mile solution. Travel-related attitudes tend to play a more significant role in travel satisfaction with dockless bike-sharing than residential built environment. Travelers with a preference for bicycles and public transport and those who value the health or environmental influence of travel tend to evaluate dockless bike-sharing travel as more satisfying. [ABSTRACT FROM AUTHOR]

Abstract: Land development impacts of mass transit have long been studied in the developed economies. Yet relatively little is known by the outside world about the Chinese experience due to China''s rather short history in the development of modern mass transit and land/property market. This paper attempts to fill the gap by presenting evidence from China, with a detailed case study of Beijing. Selecting three newly built suburban transit lines in Beijing, the study examined land development context and estimated hedonic housing price models to measure the proximity premiums associated with these three lines. The empirical evidence in Beijing, one of the first tier mass transit cities in China, shows that investments in mass transit can have significant and positive impacts on land development. Properties with transit proximity enjoy sizable price or value premiums. The study also confirms the international experience: transit impacts on land development are unlikely to occur automatically; they rely greatly on supportive regional and site conditions. Integrated planning and design for mass transit and land development are critical to expand and maximize the return of transit investments. [Copyright &y& Elsevier]

Abstract: Bus Rapid Transit (BRT) systems are undergoing rapid development in many countries due to the unique characteristics of this form of mass transit. In China, BRT schemes are being adopted as a key strategy for relieving traffic problems. As a case-study, this paper examines the performance and impacts of BRT in Beijing, the first full-featured BRT system in China. It considers in turn the role of ITS technology in influencing the operational efficiency, technical performance and cost issues associated with BRT. Particular attention is given to the consequent impacts of BRT on travel behaviour change, traffic environment and property development. While some challenges remain, the early performance of BRT suggests that it is one of the key measures for promoting sustainable mobility. [Copyright &y& Elsevier]

Abstract: This paper contributes to the study of planning for low-carbon cities by providing empirical evidence on the relationship between planning parameters (i.e., density, land use mix, accessibility to public transportation and jobs-housing balance) and household carbon emission in Beijing, China. Five neighborhoods representing different levels of carbon emission are selected using a household survey dataset that includes 1227 sample households; these neighborhoods are analyzed using the survey dataset together with land use data provided by the Beijing Municipal Urban Planning Commission. Findings confirm that higher population density, mixed land-use patterns, better accessibility to public transportation, and job-housing balance are important planning parameters that reduce household carbon emission in Beijing. This research provides the first empirical evidence from a large Asian city that can assist planners and policy makers in estimating the impact that planning principles can have on reducing household carbon emission. [Copyright &y& Elsevier]

Abstract: As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities. [Copyright &y& Elsevier]

Bicycle-sharing is an eco-friendly transportation operating model in the context of "Internet Plus" and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents' bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents' demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future. • The temporal distribution of bicycle-sharing demand is imbalanced, including between working and nonworking days, and between peak and off-peak periods of a day. • The spatial distribution of bicycle-sharing demand is imbalanced across the various administrative districts and functional zones. • Factors such as income and pass facilities have positive influence on bicycle-sharing demand, but the functional zones such as airports and ports have negative influence. • The use of bike-sharing is sensitive to the weather and air quality. [ABSTRACT FROM AUTHOR]

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services. [ABSTRACT FROM AUTHOR]

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers' commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers' trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines. • An efficient and effective data mining procedure is developed to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines. • An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. • A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. • Experimental results indicated by strong association rules provide a better understanding of transfer coordination in urban rail transit networks. [ABSTRACT FROM AUTHOR]

Massive electronic trip records have recently been utilized to infer people's trips for healthcare. Many inferential methods were developed to derive healthcare trips by taxi using GPS trajectory records, but little attention is paid to public transit, as a common travel mode for healthcare. This paper proposes a method to fill this gap by mining a big data of smart transit cards with spatio-temporal constraints. We demonstrate and validate this method in Beijing, China. The inferred trips achieve a high degree of consistency, in space and time, with empirically observed trips from a survey. The inferred trips are further used to identify spatial disparities in transit-based access to healthcare, which might have been overlooked by health policy makers. [ABSTRACT FROM AUTHOR]

• Propose novel score function for PFSs. • Develop novel entropy measure for PFSs. • Put forward novel knowledge measure for PFSs. • Propose novel picture fuzzy MULTIMOORA method. • A practical case of car sharing station site selection in Beijing is given. The car sharing is an effective supplement to public transportation systems. However, how to select an appropriate site for building a car sharing station is a big challenge. Therefore, in this paper, a novel picture fuzzy multi-criteria decision making (MCDM) model is proposed to solve the site selection problem for car sharing stations. To this end, the definition of picture fuzzy sets is adopted to accurately portray the voting information. Then, a concept of knowledge measure is defined for PFNs and an objective method based on knowledge measure is proposed to derive the weight information of criteria. To rank the candidate sites for car sharing stations, an extended MULTIMOORA method based on novel score function and Borda rule is devised. The proposed MCDM model is applied to solve the site selection for car sharing stations in Bejing. Finally, comparative analyses are given to verify the superiority of the proposed MCDM model. [ABSTRACT FROM AUTHOR]

• A multiscale simulation method and an urban modeling-design framework are presented for analyzing and evaluating transport infrastructure and public space urban designs. • A mesoscale agent-based model, traffic emission models, and microclimate simulations are coupled to develop the simulation method. • A two-fold evaluation is conducted in a case study in Beijing, including mesoscale traffic-related emissions and the resulting air pollution at pedestrian level, in line with the microclimate of public open spaces and thermal comfort. • A holistic urban design strategy is helpful for achieving both an integration within the transport infrastructure and public space system and an external integration with the ecosystem. • The findings are valuable for supporting urban designers and planners to build an environmentally- and pedestrian- friendly integrated transport infrastructure and public space plan. Traditional urban and transport infrastructure planning that emphasized motorized transport has fractured public space systems and worsened environmental quality, leading to a decrease in active travel. A novel multiscale simulation method for supporting an integrated transportation infrastructure and public space design is presented in this paper. This method couples a mesoscale agent-based traffic prediction model, traffic-related emission calculation, microclimate simulations, and human thermal comfort assessment. In addition, the effects of five urban design strategies on traffic pollution and pedestrian level microclimate are evaluated (i.e., a "two-fold" evaluation). A case study in Beijing, China, is presented utilizing the proposed urban modeling-design framework to support the assessment of a series of transport infrastructure and public space scenarios, including the Baseline scenario, a System-Internal Integration scenario, and two External Integration scenarios. The results indicate that the most effective way of achieving an environmentally- and pedestrian- friendly urban design is to concentrate on both the integration of transport infrastructure and public space to mitigate system externalities (e.g., air pollution and heat exhaustion). It also demonstrates that the integrated blue-green approach is a promising way of improving local air quality, micro-climatic conditions, and human comfort. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study concerning a special type of rail corrugation occurring on Beijing metro. Field observations, investigations and measurement results show that the dynamic behavior of the train-track interaction is responsible for corrugations. When the train speed is more than 60 km/h, as is the case for Beijing metro, the train/track dynamic interactions at the dominant frequencies are recognized to be the cause for corrugation. The natural frequencies of the Egg system track structure were identified by the hammer test, which correlated well with the dynamic response frequency spectrum of the rail when the metro trains pass by. In order to verify the investigative results and provide a method to reduce rail corrugation, a test section was installed at a typical corrugated curve by avoiding the dominant frequencies of the track structure. When monitoring rail roughness, results show the method used is an effective way to control the development of the wavelength-fixed corrugation in the case of Beijing metro. [ABSTRACT FROM AUTHOR]