Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.     

Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Understanding modal choice for the trip to school

This paper examines the factors that lead parents to select travel modes for their children’s trip to school. It does so with reference to parents whose children attend a suburban school in Auckland, New Zealand, where walking and driving are the two main travel options. Four focus groups were conducted in order to understand parents’ transport decisions. This approach revealed the significance of the following factors: perceived distance and time constraints; concerns about children’s health and fitness, as well as their competence; road safety and congestion issues; and social norms. We conclude that the reasons leading parents to drive are manifold, and, as such, a variety of interventions is needed to promote walking.     

Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data

Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. São Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions.     

Measures to evaluate post-disaster trip resilience on road networks

The resilience of transportation networks, one of the most critical infrastructure in post-disaster situations, will have a significant influence on post-disaster operations, community resilience and business continuity. Consequently, understanding the resilience of transportation networks following a natural disaster is crucial. This research proposes a new Trip Resilience (TR) measure to assess the resilience of trips on road networks following a disaster, integrating all three dimensions of resilience, namely robustness, redundancy, and recovery. The methodological approach includes an analysis of existing transport resilience measures presented in the literature to assess their ability to quantify robustness, redundancy and recovery in terms of the proposed conceptual model. The analytical formulations of the individual component measures are then developed, or adapted from previous research, along with a means of integrating all three into a combined Trip Resilience (TR) measure. A case study methodological approach is then adopted to verify the practicality of the proposed measures using the outcomes from a transportation simulation of a hypothetical Alpine Fault Magnitude 8 (AF8) scenario. A Normalised Trip Resilience (NTR) measure is also proposed that converts the TR to a normalised scale that is easily understandable to decision-makers. Finally, in order to facilitate ranking of the post-disaster impact on districts, a new measure, namely the Equivalent daily number of Impacted Trips (EIT), is proposed. The proposed measure provides an opportunity for decision-makers to estimate and rank the trip resilience between each (group of) Origin-Destination pair(s) using pre- and post-disaster flow and travel time. The resulting measures were capable of being calculated from the outputs produced by the transportation simulation model in the case study, thereby verifying their practicality in real-world situations. The importance of including both robustness (represented by the number of eliminated trips) and redundancy (represented by increased travel time), over the horizon of the post-disaster recovery phase was highlighted. Eliminated trips contributed significantly in areas that were cut off and isolated post-disaster, due to a lack of alternative routes, and increased travel time contributed as more roads were reopened but the alternative routes resulted in increased travel distances and, consequently, travel time.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

Secrets to design an effective message on Facebook: an application to a touristic destination based on big data analysis

The objective of this research is to identify which are the key variables for designing a message in a social network that can be used by an advertiser to generate Positive/Negative Engagement. The message’s design variables have been classified into four main groups: (i) Message Tools (presence of text, images, video, labels, applications, interactive games and events calendar or others), (ii) Appropriate Message Structure (length and intelligibility), (iii) Informative Cues (links to the brand, orientation towards the product or the brand, topics relevant to the audience, and a remuneration’s promise) and (iv) Persuasive and Emotional Cues (emotional signals, valence, endorsement and influencer mentions). The focus has been a tourist destination: Brand Spain that is advertised through Facebook. A content analysis was carried out and regression analysis with optimal scaling was used on 180 Spain brand’s publications; 57,626 audience reactions to such publications; and 1361 audience comments on the Brand Spain Official Fan Page. According to our results, from the four blocks of predictive variables, only two of them are useful to predict Positive/Negative Engagement: (i) the use of Message Tools (videos), and (ii) the use of informative cues (relevant topics, links on posts, and post’s orientation towards product).     

Cultural comparison of tourists' safety perception in relation to trip satisfaction

Although safety and security are seen as a priori for a prosperous tourism industry in any destination, safety and security perception and its influence on tourist behaviour have received limited empirical attention from researchers. This study investigated tourists' perception of the safety and security of a destination in relation to the level of satisfaction with their trip, using Turkey and its visitors as a case in point. The stepwise regression analysis was employed to investigate the relationship between trip satisfaction, safety and security‐related variables, existence of product or service failure, and selected demographic variables. Results showed that positive perception of Turkey's hygiene and health, and safety and security helped visitors to have greater trip satisfaction, especially if they stayed longer and enjoyed their trip without any product or service failure; however, German tourists and tourists with a secondary school degree are more likely to have less trip satisfaction. Practical implications and future research suggestions are discussed. Copyright © 2009 John Wiley & Sons, Ltd.     

Determinants of collective transport mode choice and its impacts on trip satisfaction in urban tourism

The continuous growth of tourism has important environmental impacts and transports account for a large proportion of the CO₂ emissions generated by tourists. Understanding the motivations and characteristics of collective transport users in contemporary cities may contribute to promote more sustainable forms of tourism. Based on an extensive questionnaire to international tourists in Barcelona, this study employs a multinomial logistic regression to explore the links among visitors' characteristics, motivations, and means of transportation, while an ordinal logistic regression is applied to investigate whether the preference for collective transport has an impact on the satisfaction with the trip. The novelty of our approach is testing the hypothesis that the choice of collective transports is more related to trip motivations (professional, leisure, or personal) than to socio-demographic or personal characteristics of tourists. The results show that professional travelers are more oriented to the use of private cars, but they prefer collective transports when the length of stay is higher and combined with other trip motivations. Also, using collective transports is linked to high satisfaction with the visit for the tourists using this form of transportation. This study puts forward policy implications and suggestions for future research directions, in particular regarding the utilization of non-motorized forms of transportation cities.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Optimization approach to depot location and trip selection in one-way carsharing systems

In this paper, we present an optimization approach to depot location in one-way carsharing systems where vehicle stock imbalance issues are addressed under three trip selection schemes. The approach is based on mixed-integer programming models whose objective is to maximize the profits of a carsharing organization considering all the revenues and costs involved. The practical usefulness of the approach is illustrated with a case study involving the municipality of Lisbon, Portugal. The results we have obtained from this study provided a clear insight into the impact of depot location and trip selection schemes on the profitability of such systems.     

顾炎武北游不归的地域倾向性探析

顾炎武出生于江南,却在北游活动中把下半生交付给北方,并终老于此。顾炎武之所以走上北游的不归路,他的地域倾向性(即钟爱北方,尤其是西北,而对南方心存偏见)是众多原因之一。探寻这种地域倾向性产生的原由所在,笔者认为,主要有魏晋以降北方学术文化观的影响、顾氏特立独行的性格以及顾氏个人的人生经历三个方面原因。     

Mapping ridership using crowdsourced cycling data

Cycling volumes are necessary to understand what influences ridership and are essential for safety studies. Traditional methods of data collection are expensive, time consuming, and lack spatial and temporal detail. New sources have emerged as a result of crowdsourced data from fitness apps, allowing cyclists to track routes using GPS enabled cell phones. Our goal is to determine if crowdsourced data from fitness apps data can be used to quantify and map the spatial and temporal variation of ridership. Using data provided by Strava.com, we quantify how well crowdsourced fitness app data represent ridership through comparison with manual cycling counts in Victoria, British Columbia. Comparisons are made for hourly, AM and PM peak, and peak period totals that are separated by season. Using Geographic Information Systems (GIS) and a Generalized Linear Model we modelled the relationships between crowdsourced data from Strava and manual counts and predicted categories of ridership into low, medium, and high for all roadways in Victoria. Our results indicate a linear association (r² 0.40 to 0.58) between crowdsourced data volumes and manual counts, with one crowdsourced data cyclist representing 51 riders. Categorical cycling volumes were predicted and mapped using data on slope, traffic speeds, on street parking, time of year, and crowdsourced ridership with a predictive accuracy of 62%. Crowdsourced fitness data are a biased sample of ridership, however, in urban areas the high temporal and spatial resolution of data can predict categories of ridership and map spatial variation. Crowdsourced fitness apps offer a new source of data for transportation planning and can increase the spatial and temporal resolution of official count programs.     

Challenges for ground operations arising from aircraft concepts using alternative energy

Current research in the field of future aircraft concepts aims at accommodating ambitious reduction goals set by national and international regulators. These concepts should be investigated not only with regard to aircraft efficiency, but also in terms of their compatibility with airline operations, existing ground handling procedures and airport infrastructure requirements, as these influence the overall performance of a future aircraft concept. This paper addresses this aspect, focusing on case studies concerning hybrid-electric and universally-electric aircraft concepts, analyzing implications for current ground handling operations at the airport. Current bottlenecks, such as capacity shortages, and potential areas of improvement are identified based on a state-of-the-art reference ground handling process. To this end, requirements of different stakeholders, including airports, airlines and ground handling providers, are outlined. In the next step, insights are contrasted with operational requirements of the future aircraft concepts under consideration. The paper stresses the anticipated challenges involved in aligning future aircraft requirements with current procedures, discusses the necessary adaptions to operational processes. The results highlight changes that need to be made to the current system before an aircraft can enter service, and provide an initial basis for the strategic planning of the stakeholders involved.     

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns.     

Analyzing airlines market service using panel data

Quarterly panel data covering the largest 10,000 city-pairs in the domestic US from 1998 are used to investigate airlines market entry and exit decisions. Several models are estimated looking at changes in the number of carriers serving in a market. The influence of a number of markets characteristics is examined, including number of passengers, average fare, average yield, service concentration, great circle distance, and seasonality. The results suggest that airlines are more likely to enter a market when market concentration is high and there are high average fares. Also airlines tend to enter new markets in the second quarter, then in the fourth quarter, and then in the third quarter of the year.     

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai,China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.     

Visualising where commuting cyclists travel using crowdsourced data

Encouraging more cycling is increasingly seen as an important way to create more sustainable cities and to improve public health. Understanding how cyclists travel and how to encourage cycling requires data; something which has traditionally been lacking. New sources of data are emerging which promise to reveal new insights. In this paper, we use data from the activity tracking app Strava to examine where people in Glasgow cycle and how new forms of data could be utilised to better understand cycling patterns. We propose a method for augmenting the data by comparing the observed link flows to the link flows which would have resulted if people took the shortest route. Comparing these flows gives some expected results, for example, that people like to cycle along the river, as well as some unexpected results, for example, that some routes with cycling infrastructure are avoided by cyclists. This study proposes a practical approach that planners can use for cycling plans with new/emerging cycling data.