Speed,travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data

Very little is known about cyclist speeds and delays at the disaggregate level of each road segment and intersection in an entire city network. Speeds and delays serve as vital information for planning, navigation and routing purposes including how they differ for different times of the day and across road and bicycle facility types, after controlling for other factors. In this work, we explore the use of recent GPS cyclist trip data, from the Mon RésoVélo Smartphone application, to identify different performance measures such as travel time, speed and delay at the level of the entire network of roads and intersections on the island of Montreal. Also, a linear regression model is formulated to identify the geometric design and built environment characteristics affecting cyclist speeds on road segments. Among other results, on average, segment speeds are greater along arterials than on local streets and greater along segments with bicycle infrastructure than those without. Incorporating different measures of cyclist personality in the models revealed that the following characteristics all affect cyclist speeds along segments, each cyclist’s average speed on uphill, downhill and level segments as well as geometric design and built environment characteristics. The model results also identify that the factors that increase cyclist speeds along segments include, segments which have cyclists biking for work or school related purposes, segments used during morning peak and segments which do not have signalized intersections at either end.     

Examining the effects of the built environment and residential self-selection on commuting trips and the related CO2 emissions: An empirical study in Guangzhou,China

Numerous studies have established the link between the built environment and travel behavior. However, fewer studies have focused on environmental costs of travel (such as CO₂ emissions) with respect to residential self-selection. Combined with the application of TIQS (Travel Intelligent Query System), this study develops a structural equations model (SEM) to examine the effects of the built environment and residential self-selection on commuting trips and their related CO₂ emissions using data from 2015 in Guangzhou, China. The results demonstrate that the effect of residential self-selection also exists in Chinese cities, influencing residents’ choice of living environments and ultimately affecting their commute trip CO₂ emissions. After controlling for the effect of residential self-selection, built environment variables still have significant effects on CO₂ emissions from commuting although some are indirect effects that work through mediating variables (car ownership and commuting trip distance). Specifically, CO₂ emissions are negatively affected by land-use mix, residential density, metro station density and road network density. Conversely, bus stop density, distance to city centers and parking availability near the workplace have positive effects on CO₂ emissions. To promote low carbon travel, intervention on the built environment would be effective and necessary.     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.     

Effects of household structure and selected travel characteristics on trip chaining

This paper analyzes trip chaining, focusing on how households organize non-work travel. A trip chaining typology is developed using household survey data from Portland, Oregon. Households are organized according to demographic structure, allowing analysis of trip chaining differences among household types. A logit model of the propensity to link non-work trips to the work commute is estimated. A more general model of household allocation of non-work travel among three alternative chain types — work commutes, multi-stop non-work journeys, and unlinked trips — is also developed and estimated. Empirical results indicate that the likelihood of linking work and non-work travel, and the more general organization of non-work travel, varies with respect to household structure and other factors which previous studies have found to be important. The effects of two congestion indicators on trip chaining were mixed: workers who commuted in peak periods were found to have lower propensity to form work/non-work chains, while a more general congestion indicator had no effect on the allocation of non-work trips among alternative chains.     

An exploration of the relationships between socioeconomics,land use and daily trip chain pattern among low-income residents

Daily trip chain complexity and type choices of low-income residents are examined based on activity travel diary survey data in Nanjing, China. Statistical tests reveal that non-work trip chain complexity is distinctly distinct between low-income residents and non-low-income residents. Low-income residents are inclined to make simple non-work chains. Two types of econometric models, a stereotype logit model and mixed logit model, are then developed to investigate the possible explanatory variables affecting their trip pattern. The number of stops within a chain and chain types are considered as dependent variables, while independent variables include household and personal characteristics as well as land use variables. Results show that once convenient and flexible conditions are supplied, low-income residents are more likely to make multiple activities in a trip chain. Areas with high population and employment densities are associated with complex work trip chains and more non-work activity involvement.     

Explaining the “immigrant effect” on auto use: the influences of neighborhoods and preferences

Since immigrants will account for most urban growth in the United States for the foreseeable future, better understanding their travel patterns is a critical task for transportation and land use planners. Immigrants initially travel in personal vehicles far less than the US-born, even when controlling for demographics, but their reliance on autos increases the longer they live in the US. Cultural or habitual differences, followed by assimilation to auto use, could partly explain this pattern; and it may also be partly due to changes in locations and characteristics of home and work neighborhoods. Previous studies have rarely investigated non-work travel, and have not tested workplace land use measures, compared the relative influences of enclave and home neighborhood measures, or looked at the role of culturally-bound residential preferences or motivations for migration. This study relies on a unique and rich dataset consisting of a survey of US residents born in South Asia, Latin America, and the US, joined to spatial information in a GIS. I find that the home built environment is the most consistently influential factor in explaining the lower auto use of both recent and settled Latin American immigrants. Indian immigrants use autos less than would be expected given their home and work neighborhoods. There is little evidence that either ethnic enclaves, or cultural differences, play a role in lower auto use by immigrants. These results suggest there may be a role for neighborhood built environment policies in delaying immigrant assimilation to auto use in the US.     

Linking land use with household vehicle emissions in the central puget sound: methodological framework and findings

A leading cause of air pollution in many urban regions is mobile source emissions that are largely attributable to household vehicle travel. While household travel patterns have been previously related with land use in the literature (Crane, R., 1996. Journal of the American Planning Association 62 (1, Winter); Cervero, R. and Kockelman, C., 1997. Transportation Research Part D 2 (3), 199–219), little work has been conducted that effectively extends this relationship to vehicle emissions. This paper describes a methodology for quantifying relationships between land use, travel choices, and vehicle emissions within the Seattle, Washington region. Our analysis incorporates land use measures of density and mix which affect the proximity of trip origins to destinations; a measure of connectivity which impacts the directness and completeness of pedestrian and motorized linkages; vehicle trip generation by operating mode; vehicle miles/h of travel and speed; and estimated household vehicle emissions of nitrogen oxides, volatile organic compounds, and carbon monoxide. The data used for this project consists of the Puget Sound Transportation Panel Travel Survey, the 1990 US Census, employment density data from the Washington State Employment Security Office, and information on Seattle’s vehicle fleet mix and climatological attributes provided by the Washington State Department of Ecology. Analyses are based on a cross-sectional research design in which comparisons are made of variations in household travel demand and emissions across alternative urban form typologies. Base emission rates from MOBILE5a and separate engine start rates are used to calculate total vehicle emissions in grams accounting for fleet characteristics and other inputs reflecting adopted transportation control measures. Emissions per trip are based on the network distance of each trip, average travel speed, and a multi-stage engine operating mode (cold start, hot start, and stabilized) function.     

An integrated assessment of factors affecting modal choice: towards a better understanding of the causal effects of built environment

This paper discusses the methodological challenges in understanding causal relationships between urban form and travel behavior and uses a holistic quasi-experimental approach to investigate the separable marginal influence of each of several urban form factors on mode choice as well as the complex relationships between those factors and a wide range of personal traits. Data analysis and models are used to reveal the effect of such interactions on mode choice for both work and non-work trips in Rome, Italy. It is found that population density does not have a significant marginal positive effect on sustainable mode choice for work trips. Conversely, this factor decreases sustainable mode choice for non-work trips. Small scale street design quality alone increases sustainable mode choice for non-work trips. This is while presence of street network integration alone increases automobile use for all trip purposes. The results point to the importance of incorporating all the urban form factors of diversity, design and street network integration if the goal is to increase the use of more sustainable modes of transportation for both work and non-work trips, but also show that attitudes and preferences can modify the response to urban design factors. The findings suggest that thoughtful policies triggering certain attitudes (cost sensitivity, sensitivity to peer pressure regarding the value attributed to sustainable transportation, and transit preference) can be adopted to significantly increase sustainable mode choice even in the neighborhoods with specific physical restrictions.     

Travel time estimation for urban road networks using low frequency probe vehicle data

The paper presents a statistical model for urban road network travel time estimation using vehicle trajectories obtained from low frequency GPS probes as observations, where the vehicles typically cover multiple network links between reports. The network model separates trip travel times into link travel times and intersection delays and allows correlation between travel times on different network links based on a spatial moving average (SMA) structure. The observation model presents a way to estimate the parameters of the network model, including the correlation structure, through low frequency sampling of vehicle traces. Link-specific effects are combined with link attributes (speed limit, functional class, etc.) and trip conditions (day of week, season, weather, etc.) as explanatory variables. The approach captures the underlying factors behind spatial and temporal variations in speeds, which is useful for traffic management, planning and forecasting. The model is estimated using maximum likelihood. The model is applied in a case study for the network of Stockholm, Sweden. Link attributes and trip conditions (including recent snowfall) have significant effects on travel times and there is significant positive correlation between segments. The case study highlights the potential of using sparse probe vehicle data for monitoring the performance of the urban transport system.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Exploring the influence of built environment on Uber demand

Ride-sourcing services have made significant changes to the transportation system, essentially creating a new mode of transport, arguably with its own relative utility compared to the other standard modes. As ride-sourcing services have become more popular each year and their markets have grown, so have the publications related to the emergence of these services. One question that has not been addressed yet is how the built environment, the so-called D variables (i.e., density, diversity, design, distance to transit, and destination accessibility), affect demand for ride-sourcing services. By having unique access to Uber trip data in 24 diverse U.S. regions, we provide a robust data-driven understanding of how ride-sourcing demand is affected by the built environment, after controlling for socioeconomic factors. Our results show that Uber demand is positively correlated with total population and employment, activity density, land use mix or entropy, and transit stop density of a census block group. In contrast, Uber demand is negatively correlated with intersection density and destination accessibility (both by auto and transit) variables. This result might be attributed to the relative advantages of other modes – driving, taking transit, walking, or biking – in areas with denser street networks and better regional job access. The findings of this paper have important implications for policy, planning, and travel demand modeling, where decision-makers seek solutions to shape the built environment in order to reduce automobile dependence and promote walking, biking, and transit use.     

Park-and-ride lot choice model using random utility maximization and random regret minimization

This research aims to understand the park-and-ride (PNR) lot choice behaviour of users i.e., why PNR user choose one PNR lot versus another. Multinomial logit models are developed, the first based on the random utility maximization (RUM) concept where users are assumed to choose alternatives that have maximum utility, and the second based on the random regret minimization (RRM) concept where users are assumed to make decisions such that they minimize the regret in comparison to other foregone alternatives. A PNR trip is completed in two networks, the auto network and the transit network. The travel time of users for both the auto network and the transit network are used to create variables in the model. For the auto network, travel time is obtained using information from the strategic transport network using EMME/4 software, whereas travel time for the transit network is calculated using Google’s general transit feed specification data using a backward time-dependent shortest path algorithm. The involvement of two different networks in a PNR trip causes a trade-off relation within the PNR lot choice mechanism, and it is anticipated that an RRM model that captures this compromise effect may outperform typical RUM models. We use two forms of RRM models; the classical RRM and µRRM. Our results not only confirm a decade-old understanding that the RRM model may be an alternative concept to model transport choices, but also strengthen this understanding by exploring differences between two models in terms of model fit and out-of-sample predictive abilities. Further, our work is one of the few that estimates an RRM model on revealed preference data.

     

Studying the benefits of carpooling in an urban area using automatic vehicle identification data

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode.     

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodernographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical results support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodemographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical result support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

Role of the built environment on mode choice decisions: additional evidence on the impact of density

Density is a key component in the recent surge of mixed-use neighborhood developments. Empirical research has shown an inconsistent picture on the impact of density. In particular, it is unclear whether it is the density or the variables that go long with density that affect people’s travel behavior. Many existing studies on density neglect confounding factors, for example, residential self-selection, generalized travel cost, accessibility, and access to transit stations. In addition, most still use a single trip as their observation unit, even though trip chaining is well recognized. The goal of this paper is to assess the role of density in affecting mode choice decisions in home-based work tours, while controlling for confounding factors. Using the dataset collected in the New York Metropolitan Region, we estimated a simultaneous two-equation system comprising two mutually interacting dependent variables: car ownership and the propensity to use auto. The results confirm the role of density after controlling for the confounding factors; in particular, employment density at work exerts more influence than residential density at home. The study also demonstrates the importance of using tour as the analysis unit in mode choice decisions. The study advances the field by analyzing the role of the built environment on home-based work tours. New knowledge is obtained in the relative contribution of density vs. a set of correlated factors, including generalized travel cost, accessibility, and access to transit stations.

Implications of congestion charging for departure time choice: Work and non-work schedule flexibility

The objective of this paper is to investigate the potential impacts of implementing variable congestion charging on the peak spreading of departure time choices, taking into account levels of scheduling flexibility of individuals. In particular, this study addresses non-work activities as well as socio-economic characteristics and their influence on scheduling flexibility for work trips. Departure time choice models were calibrated using data collected as part of a larger survey on the consequences of congestion charging on travel choices in the city of Edinburgh. The inclusion of variables related to work and non-work scheduling, as well as socio-economic variables have improved the performance of the models. This suggests that non-work activities, as well as work schedule flexibility have an impact on departure time choice for the journey to work. This means that even for those with flexible work schedules, but with other non-work commitments, the timing of their work trip may not be so flexible. Therefore, for the success of variable congestion charging schemes, other complimentary measures should be introduced in parallel. These include, for example, child care provision at work, opening hours of shops and leisure facilities.