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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.

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.

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.

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 km2. 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 (Tact), theoretical minimum (Tmin) and maximum (Tmax) commutes. It shows that Shanghai's commuting pattern is far from the extremes (indicated by Tmax and Tmin 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 km2). The cumulative distribution of the Tact and Tmin 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.

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.

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.

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.