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.     

Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

Recent success of bicycle-sharing systems (BSS) have led to their growth around the world. Not surprisingly, there is increased research towards better understanding of the contributing factors for BSS demand. However, these research efforts have neglected to adequately consider spatial and temporal interaction of BSS station's demand (arrivals and departures). It is possible that bicycle arrival and departure rates of one BSS station are potentially inter connected with bicycle flow rates for neighboring stations. It is also plausible that the arrival and departure rates at one time period are influenced by the arrival and departure rates of earlier time periods for that station and neighboring stations. Neglecting the presence of such effects, when they are actually present will result in biased model estimates. The major objective of this study is to accommodate for spatial and temporal effects (observed and unobserved) for modelling bicycle demand employing data from New York City's bicycle-sharing system (CitiBike). Towards this end, spatial error and spatial lag models that accommodate for the influence of spatial and temporal interactions are estimated. The exogenous variables for these models are drawn from BSS infrastructure, transportation network infrastructure, land use, point of interests, and meteorological and temporal attributes. The results provide strong evidence for the presence of spatial and temporal dependency for BSS station's arrival and departure rates. A hold out sample validation exercise further emphasizes the improved accuracy of the models with spatial and temporal interactions.     

Identification of land-use characteristics using bicycle sharing data: A deep learning approach

Extensive research has shown that urban land-use characteristics, including resident, work, consumption, transit, etc., are significantly interrelated with travel behaviors and travel demands. Many research efforts have been made to evaluate the impact of land use planning or policies on travel behavior, however, few studies are able to quantitatively measure the land-use characteristics based on the data of travel behaviors or travel demand. In this paper, a new hybrid model that combines time series feature extraction and deep neural network is proposed to identify regional land use characteristics and quantify land use intensity using ridership data of bicycle sharing. This method consists of four main parts: (i) A set of land-use characteristic labels are evaluated based on planning and Geographic Information System (GIS) data. (ii) An ensemble clustering method is used to determine the segmentation points of ridership time series. (iii) The statistical characteristics of the segmented time series are extracted and used as input to the neural network. (iv) A deep neural network is established and trained based on the processed ridership features and land-use labels. In terms of data collection, ridership data of the bicycle-sharing parking spots and land-use planning data are obtained from bicycle-sharing system and planning department in San Francisco Bay Area, California U.S.A., respectively. The test results show that this approach has high accuracy for identifying land-use characteristics based on several standard evaluation measures and that the identification distribution can be well explained. The extension results further prove that the model can be applied to effectively analyze the main land-use characteristics of the region although the identification results may become unstable after 3–4 months.     

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

This study explores the role of service reliability in determining bus transit ridership. Using stop level service supply, demand, and performance data from the Los Angeles Metro bus system, I investigate whether reliability of a directional line serving a stop influences the number of passengers boarding the line at that stop, controlling for various other established factors affecting demand. This cross-sectional analysis of the variation in line boardings across about 1300 sample schedule time point bus stops served by about 300 directional bus lines over a six-month period uses a historical archive of real-time geo-referenced vehicle location data, and focuses on five different time periods, peaks and off-peaks, of a typical weekday. By evaluating two measures that capture different dimensions of bus service reliability, and by estimating a series of regression models, I find systematic evidence that higher average service punctuality (or schedule adherence) and lower variation in schedule deviation over time are associated with greater ridership, all else equal, particularly during the peak periods. This study also provides first empirical evidence that the effect of reliability on peak-period ridership is moderated by headway. The demand for reliability seems to be higher for lines with relatively longer headways. The findings indicate that service reliability influences transit mode choice and/or line/route selection, and suggest that system-wide ridership gains can be expected from reliability improvements. From an urban planning perspective, this study provides more evidence that good service quality can effectively compliment transformations in the urban fabric brought about by coordinated land use — transit plans to promote transit use.     

Elasticities of demand for leisure air travel: A system modelling approach

In this study, the UK outbound demand for leisure air travel has been examined using a demand system which takes into account the ways in which the expenditure on air fares interacts with both the expenditure on non-fare components of travel abroad and with expenditure on domestic leisure. The findings suggest that there is a strong link between total consumer expenditure and expenditure on air travel with the expenditure elasticity greater than one but that, considered in aggregate, the demand for air travel is moderately inelastic with respect to air fares alone. However, the findings also show that interactions between air travel expenditure, other costs of travel abroad, as well as expenditure on leisure activities in the UK, involve sizeable cross-price elasticities.     

A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities

Volatile driving, characterized by hard accelerations and braking, can contribute substantially to higher energy consumption, tailpipe emissions, and crash risks. Drivers’ decisions to maintain speed, accelerate, brake rapidly, or jerk their vehicle are largely constrained by their unique regional and metropolitan contexts. These contexts may be characterized by their geography, roadway structure, traffic management, driving population, etc. This study captures how people generally drive in a region using large-scale vehicle trajectory data, implying how energy is consumed and how emissions are produced in regional transportation systems. Specifically, driving performance in four U.S. metropolitan areas (Los Angeles, San Francisco, Sacramento, and Atlanta) is compared, taking advantage of large-scale behavioral data (78.7 million seconds of speed records), collected by in-vehicle global positioning systems (GPSs) as part of regional surveys. Comparative analysis shows significant regional differences in terms of volatile driving and time spent to accelerate, brake, and jerk the vehicle during daily trips. Correlates of higher volatility are also explored, e.g., battery electric vehicles show low volatility, as expected. This study proposes a novel way to compare regional driving performance by successfully turning GPS driving data into valuable knowledge that can be applied in practice by developing regional driving performance indices. The new indices can also be used to compare regional performance over time and to imply the levels of sustainability of regional transportation systems. This study contributes by proposing a way to extract useful information from large-scale driving data.     

A multi-level spatial-temporal model for freight movement: The case of manufactured goods flows on the U.S. highway networks

The spatial production, attraction, and movement of manufactured goods are vital to the economy of a region and country. The U.S. department of transportation also mandates to incorporate continuing and efficient freight movement and infrastructure into statewide and local long range planning. Studies on supply, demand, and transport of manufactured goods by firm, industry, mode, or commodity are vast in the logistics and supply chain literature. However, relatively sparse research is available on aggregated movement or freight on national, state, or local transportation networks. Better understanding and modeling freight movement on highway networks to facilitate local transportation, land use, economic development, and comprehensive planning is at the heart of freight research. Therefore, the major endeavors and novel contributions of this research include a conceptual framework proposed for freight movement research, a multi-level spatial-temporal freight model based upon the social optimum assignment for optimal “from”, “to”, “within”, and “through” freight flows of manufactured goods on the U.S. highway networks, and a set of performance measures designed to reveal states in terms of their competitive advantages in production, attraction, self-sufficiency, or cross-road. The freight flows were first visualized and highlighted by state at the U.S. level, then by county at the state level for Oklahoma, and finally by traffic analysis zone for the Tulsa metropolitan area. The spatial split of freight flows was accomplished through using freight, network, and demographic-economic databases at state, county, and zone scales.     

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

This study is to analyze passengers' choice of the mode of transportation when air transportation is in competition with high speed rail (HSR). The Seoul-Jeju route analyzed as an empirical case study, in which the construction of an undersea tunnel to connect Seoul and Jeju city by HSR has been considered. The study also included two new variables, ‘safety of transportation’ and ‘availability of duty free shopping’ in addition to traditional transport choice variables such as travel time, travel costs and frequency of service to reflect special characteristics of the market. As data gathering tools, SP techniques and mixed logit model, for analytical methodology, were utilized. The authors found that the goodness of fit of the models was improved with new variables. The models also showed that the characteristics of business passengers and leisure passengers in choosing the mode of transportation were different. Business passengers were apt to choose a safety secured mode of transportation regardless of fare while leisure passengers preferred to use duty free shops more than business passengers.     

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.     

A study on passengers' airport choice behavior using hybrid choice model: A case study of Seoul metropolitan area,South Korea

Improving explanatory power is significantly important to understand variables that affect attitudes and perceptions in the decision process. This paper estimates not only tangible attributes but also intangible perceptions and attitudes using a hybrid-choice model to study air passengers' flight choice behavior. The empirical study was conducted for the choice behavior of air passengers at Seoul Metropolitan Area, South Korea. The analysis uses a two-level Nested Logit model in order to examine which factors have more effect on passengers’ choice of airport and airline simultaneously by using airport and airline choice attributes. The study also estimated the parameters in the equations relating the latent variable by using Structural Equation Model (SEM). The results indicate that the models with latent variables have improved Goodness-of-Fit when compared to classical discrete choice models and effectively capture psychological factors that affect choice behavior of passengers.     

Calculating load factors for the transatlantic airline market using supply and demand data – A note on the identification of gaps in the available airline statistics

This paper takes a critical view of the verification of load factors for the direct transatlantic airline market by combining supply and demand-data. The supply-related data originate from the Official Airline Guide, a well-known data source that contains information on scheduled flights. The demand-related data originate from the Marketing Information Data Transfer database, a data set containing information on actual airline bookings. Combining both data sets enables us to calculate the seat occupancy of direct flights between North-West Europe and the US for 2001. Based on the results, we assess the utility of the joint use of these data sources for aviation economic research, and identify various gaps in the available airline statistics.     

Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area,Indonesia

Using a panel regression model and a multidimensional three-week household time-use and activity diary, this study analyses the complexity of the day-to-day variability in individuals' activity-travel patterns by applying a multidimensional sequence alignment model. It is found that the variability between weekend and weekday pairs is much greater than between weekday-weekday pairs or weekend-weekend pairs. The variability of other household members' activity-travel patterns is found to significantly influence an individual's activity-travel patterns. The results also show that the variability in the activity-travel patterns of workers and students is greater when conducting a particular discretionary activity on weekdays. Due to performing discretionary activities more often and for longer, non-workers tend to have more predictable activity-travel patterns. Undertaking multitasking activities within different activities on weekdays significantly impacted the different degrees of variability in an individual's activity-travel patterns. Having different health and built environment characteristics also corresponds with different degrees of predictability of the activity-travel patterns, particularly in the worker/student case.     

A combined MCDM model based on DEMATEL and ANP for the selection of airline service quality improvement criteria: A study based on the Taiwanese airline industry

The aim of this study is to select airline service quality improvement criteria for the Taiwanese airline industry. Numerous factors such as low railway prices, the development of high-speed rail, increases in petroleum prices, increases in foreign airline competition, and the recent economic depression have led many airlines in Taiwan to encounter financial difficulties and even closure. Researchers have begun to identify critical service quality improvement criteria with the goal of increasing the competitive advantages of airlines. Most of the selected criteria, however, suffer from a major technical problem: all of the measurement dimensions are independent, leading to inadequate criteria for measuring service quality. Our calculations consider the interrelationships and effects among the evaluation dimensions and criteria to precisely rank and select criteria. The value of this study is that it provides airlines with a direction for measuring and improving their service quality with the goal of developing sustained competitive advantage over the long term.