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Fare policy plays an important role in transit operations and management. To better coordinate and achieve the multidimensional goals of a proposed fare adjustment policy (e.g., increasing revenue, managing demand, and improving equity), a fundamental step is to evaluate its travel pattern impacts, which helps us consider the policy in a bigger socioeconomic context. Existing studies rarely investigate the impacts of such a policy on different users' and user groups' travel patterns and transit operators' farebox revenue using longitudinal data from sources such as smartcard data. To fill this gap, we exploit 24 weeks' smartcard data from Wuhan, China, to empirically quantify those impacts. We find that (a) the fare increase had significant but varying impacts on travel patterns across users and user groups; (b) confronting the fare increase, commuter groups identified by the topic model reduced their trip frequency more but later as compared to other groups; (c) low-accessibility, long-distance, and single-destination metro riders were less sensitive to the fare increase; (d) when there was a system-wide fare increase with a distance-based structure, trip purposes and socioeconomic statuses could better predict the impacts on the travel demand and farebox revenue than spatiality. These findings indicate that increasing average fares while offering discounted tickets for frequent and/or captive riders could maintain the existing ridership and farebox revenue and possibly increase additional ridership. [ABSTRACT FROM AUTHOR]

A resilient urban system should cope with the dynamic human activities in its subsystems. Since travel allows people to have their needs satisfied in scattered locales, the dynamics of travel and its effects on people's well-being can vary across different spatial scales (including different subsystems) and over time. However, little empirical research has been done regarding these spatiotemporal variations. In this article, we introduced a trip resilience (TR) index to measure the time-varying travel characteristics, especially trip attraction changes, across different spatial scales. Using empirical data from Hong Kong, we quantified the TR indices for metro trips at a station across three spatial scales: local, neighborhood, and citywide. Then, we examined the spatial distribution of the TR and investigated which station (area) characteristics could explain the stations' TR indices for trips in different scales. We found that the TR indices and their predictors varied across the scales. The diversity of points of interest significantly predicted the TR indices across all the scales. Yet, other characteristics, such as the median age of residents, street density, working population, and provisions of parking spaces, only predicted the TR indices for trips at one or two of the scales. These findings shed light on more refined urban and transport planning strategies and policies concerning travel demand management across spatial scales in the post-COVID-19 era. [ABSTRACT FROM AUTHOR]

• Synthesize existing indicators of TODness and related (expected) outcomes. • Formulate (new) indicators of TODness and the outcomes. • Identify aspects of TODness have the most impacts on the outcomes. • Show differences of the impacts across days of a week. TODness, i.e., the extent to which the existing conditions of TOD sites meet agreed-upon TOD standards often requires substantial investment and efforts. Given this, it is legitimate and understandable that decision-makers expect as many positive outcomes from TODness (or TODs) as possible. Non-traditional data (NTD) has provided more opportunities for us to develop (new) indicators for TODness and expected outcomes that we have for TODness. NTD, could, for instance, be used to formulate indicators across more spatiotemporal resolutions and samples and measure new expected outcomes, e.g., smartphone users per hour on weekdays by metro station across a city. Based on case study of Shenzhen, China, this paper introduces indicators for TODness and expected outcomes (partially) based on NTD. Through (spatial) regressions, it identifies the specific TODness (both at the site and regional levels) or station (area) characteristics that have the most impacts on different expected outcomes on weekdays and weekends. The findings and results can on the one hand revalidate the known relationships between expected outcomes in the existing literature and TODness and on the other hand reveal new relationships between the two, which can serve as extra references for TOD-related plan formulation, evaluation and decision-making. [ABSTRACT FROM AUTHOR]

This manuscript summarizes/reviews the current trends of ride-sharing services, which can be a supplement to or replacement of conventional transit services. It argues that smartcard data can be utilized to identify low-demand transit routes, where emerging ride-sharing services such as Uber and Lyft can play a role as a supplement to, or replacement of transit services along those routes. It outlines transferrable procedures and methods that can be used to process smartcard data and to define, identify and visualize low-demand transit routes. In an empirical study, it shows that some low-demand transit routes can probably be replaced by Uber at a lower level of overall costs. But the empirical study also indicates that the replacement can be challenging in reality given issues such as unwillingness or difficulties of local transit agencies and ride-sharing service providers in sharing financial information regarding their services, latent demand for private ride-sharing services, social equity and political/legal concerns over the introduction of private ride-sharing services into the transit-service market and usage of public funding for residents/riders to use private ride-sharing services. • It summarizes/reviews the current trends of ride-sharing services. • It outlines procedures and methods to identify low-demand transit services. • It shows how Uber can replace low-demand transit services with fewer overall costs. [ABSTRACT FROM AUTHOR]

A city can be divided into areas that are served by transit and those that are not. In this study, the former is referred to as "transit-served areas (TSAs)". To quantify, monitor and visualise the TSAs of the Southeast Queensland (SEQ), this study analyses half-year smartcard data between 2012 and 2013 from TransLink, the transit agency for SEQ. Four scenarios are prescribed and four corresponding metrics (the minimum, actual, random and maximum travels) are calculated, which reflect transit riders' different levels of elasticity of distance travelled (EDT) relative to the cost of travel within or between TSAs and how transit riders could possibly travel as EDT varies. The total trips generated by or attracted to TSA and the temporal and spatial variations of these metrics across days are used to monitor TSAs, especially transit trips within or between them. The results indicate that transit trips attracted to, and generated by TSA and transit trips between TSAs vary significantly over time and across space. Across the scenarios, the temporal variance tends to be larger as EDT becomes more inelastic. The above results provide useful references for decision-makers to understand better the ranges of transit demand (by TSA) across the space and time when EDT is a variable. [ABSTRACT FROM AUTHOR]

Abstract Availability of new open/big data (NOBD) such as smartcard and General Transit Feed Specification data has provided unprecedented opportunities for transit planners and policy-analysts to conduct analyses that are highly challenging and even infeasible where only traditional data (e.g., censuses/surveys) are in presence. In this study, we first review and summarize discrete and scattering existing studies on (a) society and justice, (b) transportation/space and justice, and (c) transit fare and justice. We consider (c) as a subset of (b) and (b) as a subset of (a). We then illustrate how NOBD can supplement traditional data in the studies of the equity and spatial implications of transit fares via an exploratory study of Brisbane, Australia. Specifically, we propose and implement methods or procedures such as "trajectory rebuilding", "fare matching", "segment tagging", "desired line/stop visualisation", "commuter identification" and "scenario analysis" to show why and how transit fares could have important equity and spatial implications. In addition to empirical findings and policy recommendations, we offer some transferable methods and procedures for visualising and concretizing the aforementioned implications. [ABSTRACT FROM AUTHOR]

Abstract Commuting patterns where most if not all 'trips are optimised relative to a given distribution of jobs and housing can result in personal and socio-economic benefits. Excess commuting indicators provide useful information for academics and policy analysts to evaluate how the actual commuting pattern deviates from an optimal pattern where commuting costs are minimised. While actual commuting patterns vary from day-to-day, academic researchers have yet to quantify the temporal variation in these indicators over short time periods. This may be due to the lack of available longitudinal data as input for excess commuting indicators. This study shows that new, open and/or big data (NOBD) (e.g. smartcard data) can be exploited to serve as the input for such analysis. In this regard, our study uses half a year's worth of smartcard data from Brisbane, Australia to first derive/aggregate origins and destinations by small areas of the probable commuting trips by transit on all 122 weekdays over the study period. The study quantifies the day-to-day variation in excess commuting indicators for these trips and finds that excess commuting indicators vary considerably from one day to the next. Nevertheless, daily variations occur within a relatively consistent range which can be planned for. Our research suggests that more conscious and systematic utilisation of NOBD could change how commuting flows in cities are quantified, monitored and planned. In addition, with NOBD, we can more efficiently detect daily outliers in commuting patterns. [ABSTRACT FROM AUTHOR]

Abstract Existing studies have yet reached consistent conclusions on accessibility benefits of buses. Most existing studies have been conducted in the context of the West, where bus patronage is generally low. In this study, we used a database of 22,586 secondhand residential properties in 358 residential estates in Xiamen, China to develop four non-spatial hedonic pricing models (one standard and three Box-Cox transformed) and two spatial econometric models to quantify the effects of bus accessibility on property prices and analyze how the introduction of spatial econometric models would influence estimates of such benefits. Our findings are as follows. (1) Access to bus stops is positively correlated with property prices. This outcome is in contrast with findings of mainstream research (or conventional wisdom). For every bus stop within 500 m, the price of a property is 0.5% higher, all else being equal. (2) Bus travel times to essential destinations significantly influence housing prices. (3) Spatial econometric models that account for spatial autocorrelation outperform traditional hedonic pricing models. A few robustness check analyses further guarantee the plausibility of this study. However, the price premiums offered by bus accessibility may be gradually decreased, even eventually discarded, because of declining attractiveness for bus travel and continuous transit service enhancement in the forthcoming years. Highlights • Add an empirical study on pricing bus accessibility in a bus-served city in China • Decompose bus accessibility into local (to-bus) and regional (by-bus) accessibility • Gain a deeper understanding of the benefits arising from local and regional bus accessibility • Offer insights for value capture schemes of financing bus investments [ABSTRACT FROM AUTHOR]

Based on China’s latest national railway network planning proposal, the connectivity and accessibility indices of China’s high-speed railway network (CHSRN) in different time periods are computed to evaluate the implications of high-speed rail (HSR) for Chinese cities. An overall index for measuring the connectivity-accessibility of cities on the HSR network is proposed based on three indicators: (a) the Beta index, to reflect the connectivity of the HSR, (b) the number of reachable counties by HSR within the 500-km domain of a city, to reflect the location-based accessibility of the HSR, and (c) the population of the reachable places by HSR within the 500-km domain of a city, to reflect the potential-based accessibility of the HSR. Finally, the differences in the normalized connectivity-accessibility levels of different categories of cities are qualified to measure the impact of China’s future national HSR network on the potential development of cities. It is found that “Mid-to-Long-Term Railway Network Plan (Revised in 2016)”, if fully realized, would profoundly change the HSR connectivity/accessibility of different cities. Most notably, cities in the Yangtze River Delta would suffer the most whereas cities of the central and western regions would gain the most. This could potentially contribute to, or bring about new changes in, the socioeconomic landscapes in China. The methodological contribution of this paper is twofold. Firstly, an overall index to evaluate the comprehensive connectivity and accessibility levels of the HSR network is designed. Secondly, this paper investigates how to qualify the impact of the future HSR network on different tiers of cities in different time periods according to the change of the overall connectivity/accessibility index. [ABSTRACT FROM AUTHOR]

Based on the construction scale of China's high-speed rail network (CHSRN) between 2007–2017, this paper presents the evolution process and network characteristics over this period. Additionally, according to China's latest national railway planning proposal - “The Mid- and Long-term Railway Network Plan” issued in 2016, the development prospects and impacting factors of future CHSRN from 2018 to 2030 are analyzed. The evolutionary process and regularity of CHSRN development is evaluated with various complex network measures. It is found that the degree and eccentricity of each Tier 1 city increase over time, but the pagerank of almost all Tier 1 cities decreases from 2007–2017 to 2018–2030, and that the contribution of the Tier 1 cities to the network connections decreases from 2007 to 2030. The Chinese government would be adopting an egalitarian model to construct the CHSRN in the long-term. Moreover, during the second period, the CHSRN would form increasingly more connections between more populated Tier 2 and Tier 3 cities. From 2018–2030 the clustering coefficients of some Tier 2 and Tier 3 cities would be greater than those of Tier 1 cities. The HSR planners of China may have expected a larger share of passenger flows from the Tier 1 cities to Tiers 2 and 3 cities in the future. [ABSTRACT FROM AUTHOR]

Smartphone has become a desired product and even a necessity for more and more workers. This manuscript first proposes a framework for the effects of smartphone use on travel based on existing studies and anecdotal evidence. It then hypothesizes that usage of smartphone can increase the tolerance (or in other words, increase perceived benefits or positive utility of travel time) of a long trip (one type of “productivity effect” as per the above framework). It undertakes a survey of local residents in Beijing (valid sample n = 271) to validate the above hypothesis. Based on an ordered probit model, it examines whether and how the tolerance varies by factors such as socio-demographic characteristics, personal preference, and smartphone usage pattern. Its main findings are that: (a) smartphone usage significantly increases the tolerance of travel time; (b) the above tolerance varies across residents of different ages, preference, and employment statuses; (c) the tolerance is correlated to whether residents value the jobs-housing proximity; (d) younger, unemployed, or low-income smartphone users bear on average longer travel time than other users; (e) railway transit riders tend to see fewer productivity effects as compared to riders of other transit modes (e.g., regular bus). The findings indicate that the emergence of smartphone has some potential to ameliorate the situations of increased travel time, traffic congestion, and jobs-housing separation and to increase perceived benefits of travel time among some residents. This potential, nevertheless, is moderated by other factors such as preference, age, mode of travel, and employment status. Policy analysts and scholars need to probe further into the above tolerance and its influencing factors so as to take fuller advantage of the productivity effect of smart phones. [ABSTRACT FROM AUTHOR]

Existing jobs-housing balance studies have relied heavily if not solely on small data. Via a case study of Shanghai, this study shows how cellular network data can be processed to derive useful information, job and housing locations of commuters in particular, for those studies. Based on cellular network data, this article quantifies and visualizes Shanghai's jobs-housing balance with a much larger sample (n = 6.3 million), finer spatial resolution and greater geographic coverage than ever before. It identifies and geocodes the local commuters by Base Transceiver Station (BTS), which has on average a service area of 0.16 km 2 . After detecting jobs and housing by BTS, it aggregates them by subareas of particular interest (e.g., traffic analysis zones, inner city, suburbs and exurbs) to local planners and decision-makers. It also visualizes the traffic flows associated with the actual (T act ), theoretical minimum (T min ) and maximum (T max ) commutes. It shows that Shanghai's commuting pattern is far from the extremes (indicated by T max and T min traffic flows) and Shanghai's relative balance of jobs with respect to housing is decent (3.2 km) despite its huge population (24 million) and land area sizes (6800 km 2 ). The cumulative distribution of the T act and T min flows vary more significantly when the commuting distance is less than 6 km. In theory, there is high concentration of both jobs and housing within a 6-kilometer radius across different locales of the city. This potentially allows over 95% of all the local workers to find a job within 6 km of his/her residence or vice versa. In reality, a much lower percentage (71%) of workers can enjoy such a benefit. This can imply that there is qualitative mismatch between jobs and housing. [ABSTRACT FROM AUTHOR]

Amid the COVID-19 pandemic, face-to-face contacts decreased but still existed despite people's fear of virus infection and governments' social gathering restrictions. These interactions influenced virus transmission routes, if any and reflected people's essential social interactive demands in the city. In this article, we identified people who intentionally travel as groups (ITGs) to characterize social interactions before and amid COVID-19. To systematically understand ITGs' mobility patterns, an ITG structure was defined and measured in multiple dimensions, including composition, function, size, intensity, quality, and spatiotemporal distribution. Based on a longitudinal smartcard dataset in Hong Kong spanning the year of 2020, we operationalized the ITG structure in the local metro system and examined whether and to what degree the structure changed during the pandemic. We found that ITGs' activities fluctuated as the pandemic progressed and their changes differed across different ITG groups. The long-distance ITGs saw the most significant change. The spatial distribution of persistent ITG trips before and amid the pandemic became spatiotemporally more concentrated. Stations with similar ITG indices clustered in proximity, and features of station areas like residents' education level and quantity of commercial facilities could well predict stations' ITG indices. In other words, inequal distribution of essential facilities and opportunities could notably influence ITGs, social contacts, and socioeconomic benefits brought about by them amid COVID-19. The findings provide insights concerning both resilience management amid the crisis and the long-term planning of essential facilities and services that facilitate group-based outgoings and activities. • A concept of ITG structure was proposed to measure intentional group travel activities. • Both proprietorial and publicly available data can be used to operationalize the ITG structure and its correlates. • The ITG structure varied across space and time during the pandemic. • Spatial pattern of the ITG distribution across metro station could be well predicted by station-area features. [ABSTRACT FROM AUTHOR]

In 2011, the Ministry of Transport (MoT) of China announced to competitively select and fund at least 30 cities in their respective efforts of building a transit metropolis. Incentivized by MoT, many Chinese cities have started planning for a transit metropolis and even implementing related measures. This signifies some larger-than-ever government-led efforts towards the transit metropolis that we have heard of. Why did China/MoT initiate the transit metropolis program? Is the transit metropolis idea of MoT similar to what Robert Cervero defines in his book, which introduces the concept of transit metropolis and illustrates it using 12 exemplars? If not, why? Have Chinese cities followed the same principles or taken comparable measures proposed or identified by Cervero? Or, have they produced brand new principles or measures? If so, what is the implication? This article answers the above questions through literature review, interviews, surveys and case studies. It shows that MoT's idea of the transit metropolis differs notably from that of Cervero. Even though MoT proposes more universal and quantitative performance measures for a transit metropolis than Cervero, its perspectives and policies are still parochial. Local governments, nevertheless, have comparable principles or measures like Cervero. But compared to a transit metropolis exemplar such as Curitiba, they overlook issues such as the match between regional commuter sheds and the services/administrative boundaries of local transit-related entities, coordinated, regionalized transit services and fares, pedestrian-friendly streets and parking pricing strategies. The above indicate that more work is needed to better define, plan and implement a transit metropolis in China. [ABSTRACT FROM AUTHOR]

Using Beijing as an example, this research demonstrates that smartcard data can be used to (a) assemble the required data for excess commuting studies, and (b) visualise related results. Based on both smartcard and household travel survey data, we find that the theoretical minimum commute is considerably lower for bus users than for car users in Beijing. This suggests that there is a greater inter-mixing of jobs–housing functions (i.e., a better jobs–housing balance) associated with users of that mode compared to the corresponding land-use arrangement for car users, who locate further from the central area (Tian’anmen) than bus users. The commuting range for car users is 9.4 km greater than for bus users. Excess commuting is slightly higher for bus users (69.5%) than for car users (68.8%). Commuting capacity values are slightly lower for car users than for bus users, implying that car users consume less of their available commuting resources overall than bus users, albeit only marginally. [ABSTRACT FROM AUTHOR]

This article examines, via a case study, what efforts a university needs to make to initiate and sustain a carsharing program. It also examines how subsidies offered to employees affect the subscription to a carsharing program and the employees’ carsharing consumption. It finds that a university must offer a significant amount of support (both financial and labor) to initaite and sustain a carshsaring program. In the case study, it shows that at least an annual subsidy of $1500/vehicle needs be offered to attract a carsharing company to serve a university. In addition, commuter benefits offered by the university help attract university carsharers and stabilize the customer pool of the carsharing program. In the studied case, 34% of all the carsharers join carsharing because of commuter benefits. Reduced carsharing subsidies (e.g., decreased free carsharing hours) significantly decrease carsharing consumption but not the overall subscriptions to the carsharing program. [ABSTRACT FROM AUTHOR]

Abstract: Reducing car dependence has become an important public policy issue. This manuscript examines the issue by focusing on university students, who have not been well studied in existing literature. It proposes a confirmatory framework for studies on university students׳ commuting mode and housing choice and their determinants. It also conducts a case study based on this framework to get more insights. This case study shows or re-confirms that when compared to the employees from the same university, university students are more likely to share a residence in exchange for rent affordability, bus proximity and short commute. They are also more likely to jointly determine their housing and mode choices. Transit pass subsidies significantly influence university students’ alternative transportation use. Female students or graduate students are less likely to use alternative transportation. Undergraduate students have a shorter commute and use alternative transportation more. The above provide new implications for integrated housing–transportation planning and group-sensitive policies to increase alternative transportation usage among university students. [Copyright &y& Elsevier]

Abstract: This paper analyzes personal and car-sharing characteristics of commuters at university in Los Angeles, California. These commuters do not hold an on-campus parking permit and commute by an alternative mode other than driving alone. Each month, the university offers them 8h free use of shared vehicles across the campus. University employee car-sharers’ housing distribution is significantly different from that of their counterparts who drive to work. Commuter benefits influence not only the participation rate of a car-sharing program but also the program participants’ frequency, time and quantity of car-sharing consumption. Car-sharing is most popular among bus commuters, university students and female employees. [Copyright &y& Elsevier]

Abstract: This paper studies university students’ commute and housing behaviors using samples from Los Angeles, a place notorious for car dependence and dominance. It finds that being embedded in this place does not make university students drive alone more than their peers in other places. Being multimodal and having a discounted transit pass increase the odds of alternative modes while holding a parking permit reduces the odds of these modes. Commute distance is positively related to carpool and telecommuting. Gender, status (undergraduate vs. gradate) and age are significantly correlated to biking, walking or public transit. Students living alone are more likely to commute by driving alone than other students. Having friends and classmates living nearby increases the odds of taking public transit. Due to data constraints, this study cannot prove whether there is any correlation between information contagion and the effects of living alone and having friends and classmates living nearby on alternative mode choice. But it proposes that the issue be worthwhile of further investigations. Base on the above, the paper recommends a comprehensive travel demand management program, utilization of information contagion effects of students and promotion of multimodal commute to better promote alternative mode of commute among university students. [Copyright &y& Elsevier]

Abstract: Research on environmental justice and social inclusion suggests that high-income wage earners may have better job access due to their greater choices in both housing and transportation markets. This study compares the jobs/housing balance and mode choice of different groups of employees of a large employer (27,113 employees) and those of the “reference groups” from comparable employees working for smaller employers in Los Angeles. Based on spatial and statistical analyses, this paper finds the following: [a)] Across all employee groups, a better jobs/housing balance was accompanied by higher income, as was likelihood to patronize Travel Demand Management (TDM) programs. [b)] Employees from the large employer had more options for carpooling and thus drove alone less, even after controlling overall housing stock, residential location, annual income, and/or commute time. [c)] Across all employee groups, good jobs/housing balance did not necessarily bring about green mode choice. [d)] Comprehensive TDM measures by the large employer significantly reduced employees’ dependence on driving, even in a region where autocommuting dominates. However, these measures were costly to implement. [e)] Different employee groups favor different TDM programs, and the patterns are marked by income. The above findings suggest that shared or consolidated TDM and housing programs, which pool smaller employers, might better promote green mode choice. Participating employers may also negotiate better deals for program implementation when these programs involve third-party transit agencies and contractors. [Copyright &y& Elsevier]

Social movements such as the yearlong anti-extradition protests in Hong Kong provide useful and precious materials for us to revisit and visualize social and political significance of metro and light rail stations and their surroundings. We used a mixed-methods approach to draw various useful information from police records, newspapers, social media, and/or site audits to compile a list of large-scale protests and sit-ins occurred in Hong Kong between June and August 2019. We attempt to show (1) in high-density and transit-reliant cities like Hong Kong, transit services play an important role in people's daily mobility on both normal days and protest days; (2) on protest days, transit services, related facilities (e.g., entrances, lifts, and platforms) and surrounding areas can become the focuses/battlefields for both the government and the protesters; (3) we still know little about how protests and transit services/usage are related but smartcard data can help us fathom the issue. [ABSTRACT FROM AUTHOR]

Abrupt socioeconomic changes have become increasingly commonplace. In face of these, both institutions and individuals must adapt. Against the backdrop of the COVID-19 pandemic, suddenness, scale, and impacts of which are unprecedented as compared to its counterparts in history, we first propose transferable measures and methods that can be used to quantify and geovisualize COVID-19 and subsequent events' impacts on metro riders' travel behaviors. Then we operationalize and implement those measures and methods with empirical data from Hong Kong, a metropolis heavily reliant on transit/metro services. We map out where those impacts were the largest and explores its correlates. We exploit the best publicly available data to assemble probable explanatory variables and to examine quantitatively whether those variables are correlated to the impacts and if so, to what degree. We find that both macro- and meso-level external/internal events following the COVID-19 outbreak significantly influenced of metro riders' behaviors. The numbers of public rental housing residents, public and medical facilities, students' school locations, residents' occupation, and household income significantly predict the impacts. Also, the impacts differ across social groups and locales with different built-environment attributes. This means that to effectively manage those impacts, locale- and group-sensitive interventions are warranted. • Propose measures and methods that can be used to quantify COVID-19 and subsequent events' impacts. • Operationalize and implement those measures and methods with empirical data from Hong Kong. • Examine quantitatively which variables are correlated to those impacts and if so, to what degree. [ABSTRACT FROM AUTHOR]

• Dominant contributors to vibrancy around metro stations are identified. • TOD-ness indicators have nonlinear effects on urban vibrancy. • There are synergistic effects among TOD-ness dimensions/indicators on urban vibrancy. • Insights for tailoring nuanced planning/design criteria for vibrancy promotion in station areas are offered. Urban vibrancy can facilitate human activities and social interactions, attract capital and talent, enhance competitiveness and creativity, maintain resilience, and finally achieve a sustainable urban development. Theoretically, transit-oriented development (TOD) is beneficial for vibrancy. In practice, TOD implementation does not always lead to vibrant community life. To foster vibrancy around stations, we need to understand relationships between TOD-ness (i.e., the degree to which the current (physical) conditions of station areas meet the standards of TOD) and vibrancy. Empirical studies on this topic are scarce, despite numerous studies on built environment effects on vibrancy. Moreover, these studies are likely to overestimate/underestimate the effects as most of them neglect the pervasive nonlinearity and synergism. This research contributes to the understanding of nonlinear and synergistic effects of TOD on vibrancy by constructing gradient boosting decision tree model using multi-source data from 166 metro station areas in Shenzhen, China. Local explanations for the model indicate the following: (1) Sufficient bus services, horizontal built-up coverage, and mixed-use buildings are dominant contributors to vibrancy around metro stations. (2) TOD have nonlinear influences on vibrancy. (3) Synergistic effects are evident among/within TOD dimensions. Practical implications of the findings, such as targeted policies with nuanced planning/design criteria, are further discussed. [ABSTRACT FROM AUTHOR]

Transit-Oriented Development (TOD) degree, the correlation between transit-based accessibility and urban development, reflects to what extent TOD shapes urban form. This paper empirically examines the shifting patterns of TOD degree and its correlates by using publicly available big and/or open data (BOD) such as point of interest (POI) and OpenStreetMap data for Shenzhen, China, a city experiencing rapid growth/changes in recent decades. The BOD used contain up-to-date information of human activities in finer spatiotemporal resolutions than traditional data. The paper illustrates how BOD can be utilized to quantify TOD degree and its correlates in 2014 and 2017. The main findings are twofold. First, bus stops, a proxy for bus-based accessibility, were found to markedly influence urban development (measued by the number of POIs with social media check-ins) around a (planned) metro station in 2014, when the local metro system was still in devleopment. Second, the impacts of the metro-based accessibility (measured by the average time to the two central business districts) on urban development became larger than those of the accessibility to bus stops as the local metro system matured. It is recommended that decision-makers should systematically consider and harness those effects over time to better promote TOD degree. • It examines transit-based accessibility and urban development in Shenzhen • It uses publicly available big and/or open data • It illustrates new ways to quantify transit-based accessibility and urban development • It finds two effects of the accessibility: Path-dependence and Substitution/Enhancement. [ABSTRACT FROM AUTHOR]

• Utilizing a multi-modal two-step floating catchment area (2SFCA) method to investigate the impacts of public transport on job accessibility. • The multi-modal approach accounts for the inter-modal competition for the same set of opportunities. • Results reveal significant disadvantages in accessing job opportunities for workers who are dependent on public transport. • The consideration of inter-modal competition would strengthen the disadvantages of job accessibility by public transport. • Highlighting land use and transport policy countermeasures to improve job accessibility by public transport. Job accessibility depends on the relationship between transport system and land uses. It involves competition for the same set of opportunities among users of various transport modes. This study introduces a multi-modal approach to investigate the impacts of public transport on job accessibility. It adapts the multi-modal two-step floating catchment area (2SFCA) method to measure job accessibility. It collects empirical data to operationalize the method in the context of Shenzhen, China. Shenzhen is chosen because of public transport accounts for a significant share of the city's commuting trips and the city is a pilot city for China's Transit Metropolis Strategy. Results show that job accessibility by public transport is much poorer and more unevenly distributed than that by driving car. The former reaches its minimum in the peripheral whereas the latter is relatively high in most areas. Results reveal significant disadvantages in accessing job opportunities for low-income or migrant workers who are dependent on public transport. The consideration of inter-modal competition would further strengthen such disadvantages. Considering that there is often correlation between job accessibility by public transport and social equity, this study illustrates transferable procedures and methods to quantify and visualize such accessibility and to identify spots where there is deficiency in the supply of such accessibility. It demonstrates the importance of incorporating the inter-modal competition into the evaluation of job accessibility in multi-modal contexts. It highlights land use and transport policy countermeasures to improve job accessibility by public transport. [ABSTRACT FROM AUTHOR]

• Redefine familiar stranger in big data era (FSiBE). • Empirically operationalize FSiBE and its probable determinants. • Identity determinants of FSiBE's count and odds among metro riders in Beijing. • Synthesize and discuss significance of FSiBE in cities. Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological and public-policy implications, which have only been sporadically mentioned and/or examined in existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on familiar strangers that are defined in the traditional manner based on "small data" such as survey responses. Then we reconceptualize "familiar strangers" against the backdrop of the emergence and increased availability of big and open data. Such familiar strangers are called "familiar strangers in the big data era" (FSiBDE). After this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro stations, it is spatial structure, population distribution, and transport network that significantly influence the count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy and planning implications for operating metro services and managing metro station. [ABSTRACT FROM AUTHOR]

Equity and justice have long been central in transport policy. The emphasise on the importance of individuals' access to social and economic opportunities stresses the link between accessibility and concepts like agency and freedom of choice. It calls for a nuanced and comprehensive understanding of accessibility that considers specific contextual factors that limit people's transport choices, such as the design of public transport networks and services, government interventions, and competition between public transport companies. To promote people's potential mobility and freedom of choice, it is crucial to initially assess the spatial distribution of transport options. In this article, we synthesise the findings from existing scholarship on transport choice, reinforcing the need for (re)considering choice in transport equity. We propose a metric to evaluate the availability of transport choices by considering the trade-off between time and money. By analysing household survey responses on transport choices and transit service datasets, we investigate the spatial disparities in the availability of transport choices within a multimodal public transportation system in Hong Kong and explore how adjusting unappealing choices can potentially enhance transport equity. • Discuss transport equity issues with a relative understudied angle – transport choice. • Propose a metric to assess spatial equity of transport choices. • A realistic case study based on a multi-modal public transport network in Hong Kong. • Implications to transit choice provision and fare adjustment tactics. [ABSTRACT FROM AUTHOR]

Walking is an imperative travel mode, especially for short trips. Walking accessibility, which is defined as the ease of reaching essential destinations in the walk-in catchment area, may affect property prices because residents are more likely to be willing to pay for this attribute. In addition, different categories of public services may have varied influencing directions and magnitude. These two hypotheses are tested in this study. Taking Xiamen, China as a case study, we estimate the cumulative opportunities of public services on foot and develop a set of hedonic pricing models (more specifically, two pre-specified ordinary least squares models, four Box-Cox transformed models, and two spatial econometric models) to estimate, whether and to what extent, walking accessibility contributes to price premiums (or discounts). Using a database of 22,586 second-hand residential properties in 358 multi- or high-storey residential complexes, we find that (1) walking accessibility to public services contributes to the variations in housing prices and plays a role in determining housing prices; (2) different categories of services have vastly divergent, even opposite, influencing impacts; and (3) walking accessibility to primary schools, commercial centers, and sports and cultural centers have positive effects on house prices whereas walking accessibility to comprehensive hospitals adversely affects housing prices. Methodologically, we confirm that spatial econometric methods improve estimation accuracy and have more explanatory power relative to the standard non-spatial models. Robustness check analysis further guarantees the plausibility of this study. [ABSTRACT FROM AUTHOR]

The node-place model epitomizes metro stations as nodes in a city's transportation network and station areas as places accommodating activities in the city. According to this model, stations and station areas with balanced node and place contents/values contribute to harmony among human interactions, land uses, and (public) transportation services. The harmony is indispensable for civic engagement, quality of life, and well-being of urbanites. Most of the existing studies on the model, however, focus on transportation services and land uses. Little has been done on whether and how social contacts play an essential role in such harmony and are associated with node-place values. In this article, we therefore measure social contact in a city and introduce social contact as a third aspect to the node-place model. In particular, we introduce a method to identify group travel activities in the metro system, as a proxy for social contact, by using smartcard swipe records from Hong Kong's metro system in 2020. We then define and calculate place values, node values, and the intensity and density of social contacts by metro station or station area. We find that some stations with balanced and even stressed node-place values would have relatively low social contacts. The conventional node-place framework is not capable of capturing and evaluating realized social contacts of a metro station area. There exists a gap between realization of social contacts and the interaction potential facilitated by both node and place values of stations. Our new model allows us to better categorize different metro station areas according to their respective realized social contacts. • Realization of human interactions is a desired goal of the node-place model. • Intentional travel groups (ITGs) capture some realized social contacts of stations. • Social contact is involved into the node-place model as a third aspect. • Stations with balanced node and place values could have low ITG values. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 and subsequent pandemic containment measures have significantly affected our daily life, which has been extensively examined in the existing scholarship. However, the existing scholarship has done little on the jobs/housing relationship impacts of COVID-19. We attempted to fill this gap by using an excess-commuting approach. The approach allows us to analyse a series of jobs-housing matrices based on the location-based service big data of around fifty million individuals in the Pearl River Delta (PRD), China before and amid COVID-19. In the PRD, a zero-COVID policy was implemented, which presents a distinct and interesting context for our study. We found that after the COVID-19 outbreak: (1) residences and employment became more centrally located in downtowns, which is opposite to the suburbanization trend elsewhere; (2) in the whole PRD, the minimum and maximum commutes became smaller while the actual commute became larger, indicating the simultaneous presences of some paradoxical phenomena: a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes; (3) inter-city commutes between large cities were significantly refrained and decreased, while new inter-city commuters between smaller cities emerged; (4) it was more likely for the less-educated and female workers to see smaller minimum commutes amid COVID-19. This paper illustrates the potential of big data in the longitudinal study on jobs-housing relationships and excess commuting. It also produces new insights into such relationships in a unique context where stringent anti-COVID-19 policies have been continuously in place. [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]

Public transport with high levels of patronage is the cornerstone of a sustainable city. Passengers' feelings of safety shapes an individuals' decision to utilise public transport that are informed in large part by the other people with which the transit stations and public transit trips are shared. Some of these people are unknown to us but considered – familiar strangers – in that we visually recognise the individuals but have never spoken to them. Our empirical understanding of this subtle yet important social phenomena known to impact sense of community, place attachment and feelings of safety remain in their infancy but with emergence of disaggregate sources of big data bourne from smart card technologies there exists new opportunities to measure and visualise potential familiar strangers in transit networks. The aim of the current study is to develop a new measurement framework that moves beyond existing work that measures familiar strangers via a single volumetric measure and introduce a suite of metrics capturing the full scope of the social phenomena within a transit network. Our measurement framework and associated metrics are operationalised using smart card data from Brisbane, Australia. Results hold implications for policy and planning, and it is hoped that the framework will be redeployed across other situational and cultural contexts forming a growing set of comparative studies. • A new measurement framework for familiar stranger encounters in transit networks. • Measure and visualise spatial-temporal dynamics of familiar stranger encounters. • Explore patterns of familiar stranger encounters across both weekdays and weekends. • Explore patterns of familiar stranger encounters by transit passenger type. [ABSTRACT FROM AUTHOR]

Transit accessibility should take transit timetable into account and be time-dependent. The reason is that the maximum passenger carrying capacity of a transit station is determined by the scheduled timetable. In addition, passengers always choose departure time according to their own need, which varies with time. Based on the traditional two-step floating catchment area (2SFCA) and gravity method, this paper proposes two new methods to evaluate transit accessibility. The proposed methods are implemented to evaluate the bus accessibility of Xiamen City, China. According to the local bus timetable, a typical workday is divided into three periods. Within each time period, bus travel supply-to-demand ratios by station are calculated and then aggregated into the traffic analysis zone (TAZ). The empirical findings show that fluctuations in travel demand and the passenger carrying capacity of bus stations in different time periods make the bus accessibility significantly differ throughout the city. They also show that bus accessibility based on the extended 2SFCA model are equivalent to that based on the extended gravity model, when the total demand is relatively lower than the total passenger carrying capacity of a bus station. [ABSTRACT FROM AUTHOR]

Commuting efficiency, a measure of how effectively workers utilise their time and resources for journeys to work, is generally assessed through an excess commuting framework. This approach quantifies the disparity between the theoretical minimum and actual commutes. However, conventional methods often make oversimplified assumptions of industry homogeneity and temporal invariance, neglecting the intricate dynamics of commuting efficiency. To bridge the gap, this study scrutinises cross-year commuting efficiency across 13 industry sectors by developing a disaggregated excess-commuting framework. An analysis of substantial data encompassing 11 million commuters in Shenzhen over the period from 2017 to 2021 reveals that: (1) secondary sectors and industries requiring relatively lower skills demonstrate higher commuting efficiency compared to their tertiary, high-skilled counterparts; (2) increases in industry-related minimum and random commuting significantly contribute to the growth of commuting distances, whereas local self-contained employment helps mitigate this effect; and (3) addressing jobs-housing imbalance in absolute ratio may not necessarily reduce commuting distances for all industry sectors. Urban policy development should be specifically tailored to the unique evolution of commuting efficiency performance within individual industries, rather than adopting a generic one-size-fits-all approach. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19. • How travel changed relative to perceived infection risks across space and time remains understudied. • A station's elasticity is measured as the ratio of changes in trip length to the case footprints around the station. • Stations varied in elasticity values across space and different surges of the local pandemic. • Stations' physical attributes and socioeconomic characteristics could predict their elasticities. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper estimates CO2 emissions from China’s urban passenger transportation. [•] A “bottom-up” methodology is developed based upon passenger travel behaviors. [•] Traffic optimization can reduce 21% of the transportation energy in 2030. [•] Traffic plus urban optimization will reduce 43% of the transportation energy in 2030. [Copyright &y& Elsevier]

Vibrancy is indispensable and beneficial for revitalization of historic districts. Hence, identifying built environment predictors for vibrancy is of great interest to urban practitioners and policy makers. However, it is challenging. On the one hand, there is no consensus in selection of appropriate proxy for vibrancy. On the other hand, the built environment is multidimensional, but limited studies examined its impacts on vibrancy from different dimensions simultaneously. The Baitasi Area is a typical historic district in Beijing, China. In this study, on the basis of a long-term repeatedly measured dataset generated from the Citygrid sensors, we investigated the spatiotemporal distribution of street vibrancy in Baitasi Area and examined its built environment predictors in two seasons (i.e., summer/autumn and winter), with pedestrian volume as the proxy for vibrancy and built environment portrayed from four different dimensions (i.e., morphology, configuration, function, and landscape). We found that (1) the street vibrancy in Baitasi Area is temporally relatively evenly distributed, but with higher spatial concentration; (2) microclimate and built environment are more significant in winter than in summer/autumn; (3) street morphology and configuration features are more significant predictors than street function and landscape features; (4) generally, streets with higher point of interest (POI) diversity, higher buildings, and stronger network connection tend to have higher vibrancy. This study provides decision makers with insights in revitalizing historic districts. • Street vibrancy in Baitasi Area is temporally evenly distributed and yet with higher spatial concentration. • Effects of built environment on street vibrancy is more significant in winter than in summer/autumn. • Comparatively, day-time street vibrancy is less affected by the built environment. • Street morphology and configuration features are more significant predictors than street function and landscape features. • Streets with higher POI diversity, higher buildings, and stronger network connection tend to have higher vibrancy. [ABSTRACT FROM AUTHOR]

• Reveals the rationale for R+P programs in Mainland China. • The obstacles for private sector participation thereof. • The balance between profit and social obligations in R+P programs. • Local business environment has discouraged private sector participation. Populous Chinese cities have invested heavily in metro systems and planned proactively for transit-oriented development. Rail plus property (R+P) programs, where metro corporations engage in, and even orchestrate real estate development in or around rail station areas, have been recurrently reported among these cities. However, careful assessment of these programs is still rare in the existing literature. Built upon multiple R+P programs led by Shenzhen Metro Cooperation and/or Hong Kong Mass Transit Railway Cooperation, this article fathoms the rationale for R+P programs in Mainland China, the obstacles for oversea private investors' participation and the balance between profit making and social goods supply. It finds that R+P programs serve as alternative funding sources for expensive metro projects. It decreases municipal governments' cash flow contribution to those projects, which is mandated by the central government. In addition, local business environment for R+P projects has disadvantaged private sector participation, especially those private investors from overseas. Furthermore, public subsidy to local metro corporations is likely to persist as R+P programs have not been designed for economic value maximization; rather, they are in place because (1) they reduce the cash flow burden of the municipal government; (2) they are tasked by the municipal government to produce a considerable number of affordable public housing. [ABSTRACT FROM AUTHOR]