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The Covid-19 pandemic affected the lives of all humans in their daily routines, and the transportation sector became one of the most affected services worldwide. Different instruments are used to enable the effect of the pandemic on transportation to be measured. In this study, trip generation modelling is performed first to understand what factors trigger trip making and how they affect rail passenger flow (RPF) in general; the aim is to measure the loss in RPF due to the Covid-19 pandemic using these trip generation models. Daily RPF data from station to station in Türkiye during the years 2011–2019 are used for the trip generation models and 2016 rail passenger survey data are used for understanding the geographical characteristics of the flow. It is seen that instead of one model covering the whole country, it is more suitable to form different models for specific regions. Using region-based trip generation models, the Covid-19 effect on RPF on the mainlines was measured for the year 2020. Modelled values are anticipated numbers for the no-pandemic condition; therefore, by comparing the modelled and observed data, it is possible to understand how Covid-19 affected the traffic. [ABSTRACT FROM AUTHOR]

In this study, we investigate the difference between shared electric bicycles (e-bikes) and conventional shared bikes operated by Citi Bike in New York City. We examine differences in usage by examining summary statistics and we develop conditional autoregressive models to examine differences in factors associated with trip generation. These factors include bike infrastructure, subway proximity, area demographics, land use, and elevation. We also control for border effects by identifying peripheral stations. The percentage of e-bike trips fluctuates across hours of the day, with a peak around 3 PM but diminishes during peak commuting hours. We find mostly no significant differences in factors associated with the generation of trips between the two modes. Exceptions include a larger effect on e-bike trip generation when the daytime worker population is larger; other differences are due to the racial composition around the station area, some land use categories, and elevation differentials. [ABSTRACT FROM AUTHOR]

Our study compared observed traffic volumes in the Bus Rapid Transit (BRT-Light) corridor to those predicted using two different quasi-experimental methods. The first quasi-experimental design, referred to as interrupted time series, assumes the trends in traffic on BRT alignment from 2013 to 2017 continue through to 2019 after BRT-Light is in place. Making that assumption, traffic volume on the BRT alignment is 2514 vehicles per day lower (−7.38%) than one would expect based on the preexisting trend. The second quasi-experimental design is called a before-after design with a control group, which assumes that traffic volume on the BRT route would increase by the same percentage as traffic on parallel streets to the BRT line, that is 2.30 percent or 645 VPD between before and after. Also, BRT alignment's traffic volume was reduced by 1249 VPD or 4.78 percent more rather than outside of BRT corridor, and 17,632 VPD or 9.33 percent considering trip generation. Meanwhile, transit ridership in the corridor increased by 8687 passengers per day (122.66%) more than expected with the introduction of BRT, which helps account for the effective reduction of vehicular traffic on the streets that comprise the BRT alignment. Through the section analysis, we found that dedicated right-of-way reduced traffic effectively. Our estimates suggest that BRT-Light will result in an annual reduction of 17,886,229 pounds of CO2 emissions and 914,614 gallons of gasoline consumption. Based on these results, we conclude that BRT-Light had a positive effect on easing vehicle traffic volume and emissions in the Provo-Orem metropolitan area. • BRT-Light, which is the lower limit of BRT, had a positive effect on easing vehicle traffic and emissions on BRT alignment. • BRT-Light reduced traffic not only on the BRT alignment but also on the entire streets in the half miles-buffered area. • The dedicated right-of-way reduced traffic effectively. • BRT-Light provoked significant nearby land-use development and increased trip generation. • Significantly increased transit ridership in the corridor explained to eased vehicular traffic. [ABSTRACT FROM AUTHOR]

Jurisdictions rely heavily on traffic impact analyses (TIAs) to predict the traffic impacts of projects and calibrate appropriate mitigations. But TIAs are also litigation tools: Jurisdictions use them to satisfy courts that their land use decisions are supported by substantial evidence, or evidence that is credible and reliable. The problem, as we discuss in this Viewpoint, is that TIAs are not consistently credible and reliable. We explore some common criticisms—and provide a brief overview of a growing literature—regarding underlying vehicle estimation methods in practice that demonstrates the ways in which TIAs are widely flawed. Historically, courts have not expected much from TIAs, but our analysis shows a tipping point in which courts may begin to question whether conventional TIA methods constitute substantial evidence, suggesting an important need to innovate and adopt new data and methods in practice. [ABSTRACT FROM AUTHOR]

ABSTRACTInformation and Communication Technology (ICT) is advocated to contribute to sustainable transportation by prompting a change in travel patterns. Therefore, using an ICT-enabled city region in India as the study context, the objectives of the study were to examine the influence of ICT on travel patterns in city regions and explore the strategies to enhance ICT usage for positively changed travel patterns. Findings suggest that definite linkages between ICT usage and travel patterns exist. An increase in ICT usage in daily activities of people will enable a significant reduction in trip generation, travel distance and travel time. Strategic interventions including enhancing networking and ICT connectivity, provisioning free Wi-Fi hot spots at important nodal points, and enabling the availability of real-time road transportation-related information can enhance ICT usage leading to a positively changed travel pattern. This is likely to improve traffic movement and reduce congestion contributing to sustainable urban road transportation.

This paper presents the development of trip generation models in an emerging country. In this paper, trip generation models for a medium-size Palestinian city, the City of Jericho, are established. This study is the first comprehensive city-wide trip generation study that was done within the Palestinian territories. Moreover, the study considers mixed land use while developing trip generation models. Household demographic, socioeconomic, land use and trip data were collected to generate models to represent trip generation for trips with different purposes, such as number of daily trips for educational, shopping, social, and recreational purposes. Stratified and random household samples are drawn from the study area, covering 14 different traffic analysis zones. All the households were visited, and personal face-to-face interviews were conducted to collect demographic, socioeconomic and travel behavior information. A multivariable regression analysis approach is used to analyze the collected data in order to develop overall daily trip generation models, as well as trip generation models by trip purpose and time of day. Most of the trip generation models that were developed have acceptable coefficient of determination (R2), while some of the models did not perform as expected. The Education Trips model resulted in the highest R2, while the Off-Peak Trips model resulted in the lowest R2. Most of the independent variables were also relevant to present the expected socioeconomic characteristics. Using land use variables did not significantly contribute towards improving the regression models, where only marginal improvements are noticed. It is suggested to investigate the transferability of the developed models to other Palestinian cities, and to verify the applicability of these models to other cities with different sizes.

Information and Communication Technology (ICT) is advocated to contribute to sustainable transportation by prompting a change in travel patterns. Therefore, using an ICT-enabled city region in India as the study context, the objectives of the study were to examine the influence of ICT on travel patterns in city regions and explore the strategies to enhance ICT usage for positively changed travel patterns. Findings suggest that definite linkages between ICT usage and travel patterns exist. An increase in ICT usage in daily activities of people will enable a significant reduction in trip generation, travel distance and travel time. Strategic interventions including enhancing networking and ICT connectivity, provisioning free Wi-Fi hot spots at important nodal points, and enabling the availability of real-time road transportation-related information can enhance ICT usage leading to a positively changed travel pattern. This is likely to improve traffic movement and reduce congestion contributing to sustainable urban road transportation. [ABSTRACT FROM AUTHOR]

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This project was completed as a National Center for Sustainable Transportation graduate student research project at the University of Vermont. The work builds on the prior work of Dr. Brian H. Y. Lee and the National Center for Sustainable Transportation graduate student Sean Neely who focused on travel behavior between non-metropolitan areas and large metropolitan centers, because of its impacts on quality of life, multimodal planning, and rural economies.This project studies travel from home locations in northern New England (Vermont, New Hampshire, Maine, and Massachusetts, excluding the Boston-Cambridge-Quincy Metropolitan Statistical Area), going to Boston, New York City, Philadelphia, and Washington, DC. Data were collected in The Intercity Travel, Information, and Technology Survey Questionnaire conducted by Resource Systems Group (RSG Inc.) for the University of Vermont’s Transportation Research Center (UVM TRC) and the New England Transportation Institute (NETI), with funding from the US Department of Transportation (USDOT) NCST in May 2014. A total of 2560 valid survey responses were collected using a paid panel purchased by the consultant. In his Master’s thesis, Neely developed generic mode choice models for these intercity trips but lacked the mode-specific travel times and access measures for more advanced mode choice models. The goal of this current project was to perform more advanced spatial data re-tabulation to generate new mode-specific predictor variables, especially measures of air access using Federal Aviation Administration datasets. The internet access measures were also refined and alternative measures of a zip code location’s ruralness were generated. Zip code home location was used for generation of on-road travel times and distances to destination as well as an Amtrak station access measure.In addition to the data development, some specific research questions were pursued with the data: 1) How many trips per year do rural residents take in the Northeast United States to major metropolitan areas? And 2) What socio-economic, location, and accessibility variables are associated with rural trip generation to metropolitan areas?View the NCST Project Webpage

There is evidence that rural areas are disadvantaged in mobility compared to urban areas. Hence, this study examined spatial variation in the travel pattern of households in urban and rural areas of Nigeria. This study used primary data obtained through questionnaire administration on household heads in the residential zones of both urban and rural areas studied, using the multi-stage sampling technique. Findings revealed that variations exist for age, education level, income level, and occupation in urban and rural areas, and household's average daily mean trip frequency showed a level of fewer trips being generated in the rural area than those in the urban area. Furthermore, the result of the stepwise multiple regression analysis showed that transport mode, household size, number of workers in the house, and occupation of household head were significant variables influencing trip making in urban areas while age, household size, the income of household head and number of employed people were significant in the rural areas. This study concludes that differences exist in the mobility pattern of urban and rural households, and as such, equal consideration and attention should be given to them in policy formulations.

The socio-economic characteristics of individual and group have been found to significantly have an impact on their mobility and travel behaviour. Hence, this study examined the influence of socio-economic characteristics of households in the Rural areas of Nigeria on trip making. The study adopted the multi-stage sampling technique method where 12, 365 registered buildings were systematically selected out of a total of 28,341 buildings. Systematic sampling technique was used to survey 4 percent of households, representing 495 household heads of a total 12,365 registered buildings by means of questionnaire administration. The study revealed that majority of the respondents were between the age of 60–69 years and utilized the non-motorized (walk & bicycle) means of transport. The result of the multiple regression analysis further revealed that Age and Income were the significant socio-economic variables influencing trip making of households. This study thus advocates the influence of demographic attributes on households’ travel behaviour. Therefore, rural communities should be given equal privilege as urban areas when formulating transport policies as a result of their unique and distinct socio-economic attributes.

Urbanization is intrinsically connected to economic progress. India's rapid economic and population growth has increased its carbon footprint and traffic congestion. A long-term strategy is essential to preserve the balance and alleviate the issues arising from the expansion. Integrating land use and transportation planning has been acknowledged as a means to achieve sustainable urban development worldwide. Transit Oriented Development (TOD) is one such strategy. TOD is a planning and design strategy for promoting urban development by clustering jobs, housing, services, and amenities around public transport stations. This strategy can help achieve sustainable communities and improve the quality of life. This research paper assessed the land use characteristics of an urban fringe area in Trivandrum city and completed a land suitability analysis using GIS software tools. A potential node for re-development was identified by looking at various traffic, demographic, and land use parameters. Detailed TOD recommendations for the area surrounding the transit node were proposed based on its development potential. [ABSTRACT FROM AUTHOR]

Purpose—rapid urbanization is dangerous to the development of society as it hinders development and makes urban living more difficult. This research aims to develop a system dynamics model for land use assessment at transport interactions in Chennai. Design/methodology/approach—the parameters of urbanization can be simulated using a system dynamics model. In this paper, the unchecked urbanization of Southern Chennai is considered as a case study; various parameters, such as population, land use, trip rate, Volume/Capacity (V/C), and Demand/Supply (D/S), are simulated for three different years: 2011, 2021, and 2031. Three scenarios are simulated: the Do-Min scenario (existing trend), partial scenario (some models are implemented), and the desirable scenario (all the proposed models are implemented). Findings—the simulation is performed using the Stella simulation tool. The results show that the proposed desirable model is highly effective in controlling both the population and other parameters. Originality/value—this study has been performed in the Indian city of Chennai, where such studies are rare. This study could help to analyze the traffic situation and make improvements to ease the urbanization issues. This research could also help to analyze the future traffic situation and make improvements to ease urbanization-related issues. Moreover, when the desirable scenario is followed in real-time, it could solve future problems that may arise from traffic congestion in Chennai. [ABSTRACT FROM AUTHOR]

Ride-hailing services, which allow consumers to order and pay for rides through smart phone applications, have grown to a substantial proportion of the transportation market. Today, an estimated 15% of adults across the U.S. and 21% living in major U.S. cities have used ride-hailing services. The growth of ride-hailing services has raised questions about their overall effects on the transportation system. While they clearly offer a new form of mobility, there is concern they may increase congestion and air pollutant emissions. A limited number of studies have attempted to quanitfy changes associated with the increased use of ride hailing services.UC Davis researchers examined how ride-hailing affects the total amount of driving (measured in vehicle miles traveled, VMT) as well as greenhouse gas (GHG) emissions. The researchers developed a framework of categories for analyzing the multiple aspects of transportation that may be affected by ride-hailing. These categories are: automobile ownership; number of vehicle trips generated; choice of mode of travel; empty (passenger-less) travel between drop-off and pick-up points, known as “network travel”; and destination choice and land use. Thirteen (13) studies were analyzed using this new framework: 8 used surveys of riders or recorded data on rider and driver activity; and 5 used simulated (“modeled”) travel in and around cities by automated taxis. By compiling multiple studies in the framework, stronger and more certain conclusions could be reached.View the NCST Project Webpage

Trip generation modelling is not a standard task as the relationship between the input and output variables is not exactly known and is often nonlinear. Furthermore, the input variables may have different types especially at the disaggregate levels. Accordingly, there is no single modelling technique that can be used for all trip generation cases. In this paper, a multiple piecewise regression technique is proposed to suit most trip generation cases. Thresholds can be identified at the discrete values in the case of categorical inputs; while, no thresholds are considered in the case of binary inputs. In addition, the locations of thresholds in case of continuous inputs are obtained to minimize the summation of squared errors and achieving the logical constraints. Thus, a broken hyperplane is determined using quadratic programming. The proposed technique is illustrated using a numerical example and then validated using three well-known examples. The results show that the proposed technique can fit the given data more accurately than the previous techniques. In addition, the proposed technique leads to more explicit and visualized models for practitioners. These findings will encourage researchers to apply the proposed technique in further applications such as trip distribution and mode choice models.

This research captures the complex interaction between land use and transportation in an urban context. Even though several land use–transport interaction models at both micro and macro levels are available, there is a necessity to address the intermediate-level model with acceptable accuracy and without intensive data requirement. Further, consideration of road networks and land use in the traffic analysis zone demarcation is a significant requirement to enhance accuracy in estimating passenger flow distribution. This research provides a methodology to identify modified traffic analysis zones (MTAZs) based on traffic flow distribution and to redistribute macrolevel trip origins and destinations into MTAZs considering the road network and land-use character of the area. The core objective of the research is to develop a methodology to demonstrate the relationship between trip production/attraction and land use, to propose a method to redistribute macrolevel origin-destination data into MTAZs based on the identified relationship and to propose a method to assign estimated traffic flows to the road links. The methodology intends to guide the land use and transportation decision making. The methodology is developed based on both spatial and statistical analysis utilizing software and tools, such as ArcMAP, SPSS, and MS Excel. A case study covering the Colombo Municipal Council area, which is located within Colombo District, Western Province, Sri Lanka, is presented to demonstrate the application of the proposed methodology. According to the validation results, the proposed methodology has increased the accuracy of passenger flow estimation compared with the administrative zone-based conventional passenger flow estimation methodologies. [ABSTRACT FROM AUTHOR]

Towards the close of the first decade of the 21st Century, ride-hailing services began to enter the transportation market through smart phone applications that allowed consumers to hail and pay for a ride from drivers using their own vehicle. The information and communication technologies used by these platforms allow for more reliable service, to more locations, with shorter wait times, and at a lower cost than traditional taxi services and, perhaps, public transit. Today, an estimated 15% of adults across the U.S. and 21% in major cities have personally used these services. The successful entrance of ride-hailing services into the transportation market has raised questions about their effect on the overall transportation system, including congestion, total vehicle miles traveled (VMT), and greenhouse gas emissions (GHGs). Reliable answers are limited, in large part, because of their rapid expansion and the lack of publicly available data from these private ride-sharing companies. However, there is now a small body of research, most conducted in 2016 and 2017, that provides some initial evidence on the impacts of these services. This research includes population representative survey data, targeted ride-hailing user survey data, and measured ride-hailing driver and passenger activity data. In addition, the recent interest in automated vehicles has produced modeling studies that also provide insight into the potential effects of ride-hailing services. The following framework was developed to identify the range of possible travel effects, both positive and negative, on users of ride-hailing services. This includes the effects of ride-hailing on auto ownership, trip generation, destination choice, mode choice, network vehicle travel, and land use.View the NCST Project Webpage

The socio-economic characteristics of individual and group have been found to significantly have an impact on their mobility and travel behaviour. Hence, this study examined the influence of socio-economic characteristics of households in the Rural areas of Nigeria on trip making. The study adopted the multi-stage sampling technique method where 12, 365 registered buildings were systematically selected out of a total of 28,341 buildings. Systematic sampling technique was used to survey 4 percent of households, representing 495 household heads of a total 12,365 registered buildings by means of questionnaire administration. The study revealed that majority of the respondents were between the age of 60–69 years and utilized the non-motorized (walk & bicycle) means of transport. The result of the multiple regression analysis further revealed that Age and Income were the significant socio-economic variables influencing trip making of households. This study thus advocates the influence of demographic attributes on households' travel behaviour. Therefore, rural communities should be given equal privilege as urban areas when formulating transport policies as a result of their unique and distinct socio-economic attributes. [ABSTRACT FROM AUTHOR]

The government places emphasis on increasing the usage rate of public transportation nowadays due to public transportation having many benefits for the environment. In order to understand the key factors of trip generation and identify the key trip purposes for selecting transportation modes in a target city, the cell phone data and personal trip survey data were studied by using the machine learning methods of Association Analysis and Inverse Reinforcement Learning. Findings such as hospital, park and elementary school are the most important elements implies that the facilities for mandatory task will attract more people. Also, the elderly age group has very strong tendency to use private vehicle compared to other age groups implies that attracting more young people may be a good strategy. Findings can be a reference for new policy planning, including re-planning the exiting routes of bus systems or integrating different public transportation, by the local government. [ABSTRACT FROM AUTHOR]

In recent years, a rush of privately-owned shared micromobility services has descended on many American cities. The increased availability in these emergent mobility options, which include dockless bikeshare and electric scooter systems, offers urban residents, workers, and visitors a convenient travel alternative to more established modes. However, with limited regulation and dedicated infrastructure, the rapid introduction of new micromobility services has come with rising safety concerns. This study provides new evidence on the spatial associations between e-scooter trip generation and vulnerable road user crash counts by investigating eight months of shared mobility data collected during a 2019 pilot program in Brookline, Massachusetts. The findings from traditional and spatial negative binomial models with a set of network and environmental predictors are presented and demonstrate a connection between shared e-scooter and long-term vulnerable user crash activity. Our results illustrate the need for policies that promote shared mobility services through safer infrastructure provisions. [ABSTRACT FROM AUTHOR]

Trip generation modelling is not a standard task as the relationship between the input and output variables is not exactly known and is often nonlinear. Furthermore, the input variables may have different types especially at the disaggregate levels. Accordingly, there is no single modelling technique that can be used for all trip generation cases. In this paper, a multiple piecewise regression technique is proposed to suit most trip generation cases. Thresholds can be identified at the discrete values in the case of categorical inputs; while, no thresholds are considered in the case of binary inputs. In addition, the locations of thresholds in case of continuous inputs are obtained to minimize the summation of squared errors and achieving the logical constraints. Thus, a broken hyperplane is determined using quadratic programming. The proposed technique is illustrated using a numerical example and then validated using three well-known examples. The results show that the proposed technique can fit the given data more accurately than the previous techniques. In addition, the proposed technique leads to more explicit and visualized models for practitioners. These findings will encourage researchers to apply the proposed technique in further applications such as trip distribution and mode choice models. [ABSTRACT FROM AUTHOR]

The State of California has enacted ambitious policies that aim to reduce the state’s greenhouse gas (GHG) emissions. Some of these policies focus on reducing the amount of driving throughout the state, measured in vehicle miles traveled (VMT), given that transportation, primarily automobile use, is the largest single source of California’s GHG emissions. To encourage local plans and projects that reduce VMT, California has established several grant programs to which local jurisdictions may apply. These grant programs have generated a need for methods to estimate the potential VMT – and thus GHG – impacts of proposed planning efforts, land development projects, and transportation projects. A range of VMT estimation methods are available for use by funding applicants. Regional travel demand models, for example, are used to estimate the VMT and GHG implications of alternative scenarios in the development of federally-required regional transportation plans and state-required sustainable communities strategies. These models are resource intensive, however, requiring modeling expertise and sometimes many days to complete a single analysis. To fill the need for less resource-intensive methods more appropriate for localized plans and individual projects, upwards of a dozen “sketch” tools have been developed. These sketch tools vary in their approach and appropriateness for the breadth of development projects and project locations in the state. Practitioners are often unsure as to which method to use for a particular project and have little information to guide their choice. In this report we compare and evaluate VMT estimation tools across a sample of land use projects. We compare the results from different tools for each project, consider the applicability of methods in particular contexts and for different types of projects, and assess data needs, relative ease of use, and other practical considerations.View the NCST Project Webpage

The prediction of trip generation is an essential problem for effective traffic engineering and urban management. Traditional methods are on the large spatial scale (e.g. Traffic analysis Zone, TAZ), based on the single source and fewer types data. It is difficult to carry out refined research on smaller spatial units, due to the high aggregation of personal trip survey data. In addition, the experience-based models cannot easily capture complex non-linear relationship, which leads to lower accuracy. Multi-sources data provides the possibility to improve the prediction accuracy of trip generation. Based on the point of interest data (POI), more disaggregate spatial unit are subdivided, and grid-scale spatial correlations are taken into consideration. This paper proposes a Convolutional Neural Network-Multidimensional Long-short term memory neural network (CNN-MDLSTM) model to analyze the spatial correlation between trip generation and land use features, capture prominent features in a spatial range through the convolution structure and describe the spatial interaction using the sequence transfer structure of Long-short term memory neural network (LSTM). The deformed Multi-Dimensional Long-Short Term Memory neural network (MDLSTM) is used to adapt to the two-dimensional spatial relationship. Through case analysis and comparative analysis between models, it is shown that CNN-MDLSTM characterizes the quantitative law of trip generation and land use features better than the other neural network models. In addition, this study also discusses the output prediction accuracy of different land use grid cells and the impact of different land use characteristics on the prediction accuracy.

This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.

Estimating trip and parking generation for different land uses is vital for transportation planning, design and management purposes. This study was carried out to develop trip and parking demand for hospitals and medical clinic centers (MCCs) in Jordan. Based on field investigation and predefined criteria, fifteen hospitals and twenty MCCs were selected. Attracted and generated trips, as well as parking demand, were manually surveyed during workdays and holidays. In addition, data on characteristics of the selected sites were obtained. Based on hospital and MCC attributes, peak trip rates were computed for AM and PM periods. According to the maximum peak, rates/models were developed to estimate attracted and generated trips as well as parking demand. Compared with those used in the developed countries, values obtained in this study are relatively low, except for the value of trip generation for hospitals. Therefore, it is recommended to conduct further studies to establish a trip and parking generation manual for local use in Jordan rather than relying on manuals developed for other countries or by studies of limited scale. [ABSTRACT FROM AUTHOR]

City governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts. The proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models. [ABSTRACT FROM AUTHOR]

Project DescriptionTranSummit in conjunction with RK Engineering Group has been tasked to conduct a traffic impact study and design the traffic signals for a future mixed-use development in Aliso Viejo, CA. Existing traffic conditions were analyzed to project future traffic impacts. Following the study, possible mitigation measures will be recommended to achieve the best Level of Service. Professor: C. Stephen Bucknam Jr., P.E.Faculty Advisor: Michael McNally, Ph.DFaculty Mentor: Riju LavanyaClient Consultants (RK Engineering): Robert Kahn, P.E., Roger Goedecke, Tiffany Giordano, Mario Gutierrez, Alex TrabiziProject Manager: Jethro Jay NarcisoDesign Engineers: A. Besa, B. Elenes, P. Johnson, S. Kevorkian, E. Sy SuWinter 2015

Household structure has been recognized as an important element of residential location choice. The aim of this work is to understand how the spatial configuration resulting from these location choices could cause differentials in terms of urban life and opportunities. To do so, a weighted gravitational accessibility index is applied to the São Paulo Metropolitan Area to investigate the differences among household structures to distinct types of accessibility (jobs, education and leisure). The results point to a high disparity between accessibility levels, especially in the accessibility to jobs. The household size plays an important role, with lower accessibility levels associated with more members in the household. The study shows that the household composition also has impacts on accessibility and that higher deprivation levels affect single-parent households.

• The research is based on a historical dataset of Uber trips from September 2016 to September 2018. • Influence of lagged ridership and seasonal effect have a positive correlation with TNC demand. • TNC trips increase where the commuting trip duration is longer than 60 minutes. • Number of apartments in a dissemination area is positively correlated with TNC trip generation. • Temperature and total daily precipitations are positively correlated with TNC demand. This paper investigates the effects of the built environment and weather on the demands for transportation network companies (TNC) in Toronto. The research is based on a historical dataset of Uber trips from September 2016 to September 2018 in Toronto. A wide range of built environments, sociodemographic, and weather data are generated at the dissemination area-level and fused with the monthly aggregated Uber dataset. To provide insight into the underlying factors that affect TNC demand, a series of aggregate demand models are estimated using log-transformed constant elasticity demand functions, with consideration of the seasonal lag effect. To capture the weather effect, an autoregressive moving average model is estimated for the downtown core of Toronto. The model results show that the influence of lagged ridership and seasonal lag effect have a positive correlation with TNC demand. The trip generation and attraction models reveal that TNC trips increase where when the commuting trip duration is longer than 60 min. It is found that the number of apartments in a dissemination area is positively correlated with TNC trip generation, while the number of single-detached houses has a negative correlation. The time-series model indicates that temperature and total daily precipitations are positively correlated with TNC demand. Due to the lack of comprehensive data sources on the Uber and Lyft ridership, the policymakers often struggle to make evidence-based policy recommendations to regulate such disruptive technologies. The series of models presented in this study will help us better understand the potential users of transportation network companies (TNC) and the effects of land use, built environment and weather on transportation network company trips. [ABSTRACT FROM AUTHOR]

The road network development programme, as well as planning and design of transport systems of cities and agglomerations require complex analyses and traffic forecasts. It particularly applies to higher-class roads (motorways and expressways), which in urban areas, support different types of traffic. Usually there is a conflict between the needs of long-distance traffic, in the interest of which higher-class roads run through undeveloped areas, and the needs of bringing such road closer to potential destinations, cities [1]. By recognising the importance of this problem it is necessary to develop the research and methodology of traffic analysis, especially trip models. The current experience shows that agglomeration models are usually simplified in comparison to large city models, what results from misunderstanding of the significance of these movements for the entire model functioning, or the lack of input data. The article presents the INMOP 3 research project results, within the framework of which it was attempted to increase the accuracy of traffic generation in agglomeration model owing to the use of BigData – the mobile operator’s data on SIM card movements in the Warsaw agglomeration.

This research aims to contribute to understanding the range of site characteristics that are associated with multi-modal trip generation rates at residential developments. Data was extracted from the Trip Rate Information Computer System (TRICS) database for 933 residential developments in the United Kingdom and Ireland, with trip generation rates estimated separately for person, vehicle, public transport, pedestrian and bicycle trips. A total of 65 independent variables were included in the analysis, representing a range of site characteristics relating to location and housing attributes, public transport service quality, parking and travel plan measures. Trip generation rates by mode and time period were then regressed against the site characteristics to explore their association with multi-modal trip generation. Key findings showed that multi-modal trip generation rates are associated with a range of site characteristics at residential developments. These include, to varying degrees, locational and housing attributes such as apartment developments and housing size, population density, car ownership, distance to local facilities such as the nearest local/corner shop, public transport service quality, on-site parking spaces/dwelling, plus various travel plan initiatives such as secure well-lit/covered cycle parking. The results imply that various site characteristics deserve greater consideration in establishing multi-modal trip generation rates and that characteristics that support travel by non-vehicle modes should be incorporated within new residential developments where possible. • Data extracted from TRICS database for 933 residential developments (UK & Ireland). • Trip generation rates estimated separately by transport mode and time period. • Total of 65 variables considered, representing a range of site characteristics. • Multi-modal trip generation rates were associated with various site characteristics. • Associations with site characteristics differed by transport mode and time period. [ABSTRACT FROM AUTHOR]

Change in trip rates affects a transportation system and could lead to the redesign of the transport infrastructure in order to satisfy the new demand. This study estimates trip generation rates for residential land use in Uyo using cross classification method. Five (5) residential estates were considered and household survey carried out to collect trip data from 500 households on purpose and mode of travel through household interview and their response recorded in questionnaire. Four independent variables (household size, household income, car ownership, number of employed persons) were used for the study based on the prevailing conditions of the residential land use. Cross-classification trip rates were developed from the most significant variables; household size, household income and car ownership. The analysis indicated that work trip produced the highest reported trip rates of 29.6% followed by religious trip of 24.7%. Similarly, private car trips contributed 42.8% of trips made by mode of travel as the highest trip. It was found that household size is the strongest socio-economic variable that influence trip generation in residential land use in Uyo. The cross-classification trip rates developed in this study could provide basis for the estimation of trip generation in residential land use in Uyo. [ABSTRACT FROM AUTHOR]

In recent decades, enhancing the share of walking in individuals' daily trips has become a priority for transportation policymakers, urban planners and public health researchers as an interdisciplinary area. In this regard, determining influential factors on walking has become a matter of contention to move toward a sustainable mode of transportation. This study investigates the impact of the influencing factors on the share of walking in trip generation from/to Traffic Analysis Zones (TAZ) of a city across four trip purposes. In this study, individuals' trip information for four trip purposes has been tested in order to detect influencing factors on walking trip generation based on 112 TAZs for the city of Rasht, Iran. According to the results, density and design are found to be more influential for produced walking trips and diversity is shown to be more effective for attracted walking trips. [ABSTRACT FROM AUTHOR]

Planning for freight presents a perpetual challenge for governments. Understanding freight flows has attracted increasing research attention, though such knowledge can be difficult to translate to address problems in planning practice. This paper critically assesses the challenges of understanding and planning for urban freight movement, especially with reference to achieving more sustainable outcomes. In doing so, we elucidate guidance for broader analytical and policy approaches suited to meeting diverse public interest objectives. We review the literature with specific reference to the nature of policy problems, while collating existing conceptual knowledge for broad urban land use types. [ABSTRACT FROM AUTHOR]

Numerous cities are undergoing the ageing of populations. Developing a transport system that incorporates mobility needs, preferences, and demands of elderly people is crucial to such a demographic shift. Understanding the travel behavior and characteristics of elderly people is the first and foremost step towards this goal. In stark contrast with Western car-dominant places, numerous East Asian modern cities (e.g., Hong Kong, Beijing, and Shanghai) have high levels of public transportation use. Experience gained from existing studies in the West cannot be applied to Hong Kong, a typical transit-dependent city. In light of this, based on the 2011 Travel Characteristic Survey data and Poisson and negative binomial regression models, this study identifies factors that significantly affect the number of daily trips taken by (or trip frequency of) elderly people in Hong Kong. The paper finds that both socio-demographic (e.g., age and housing type) and built environment characteristics significantly affect the trip number of elderly people. Interestingly, it is determined that car availability does not play a significant role in the trip generation process, but public transportation accessibility truly matters. This outcome remarkably differs from findings in Western car-dominant cities. Moreover, to discern whether the elderly are a homogenous group, this study estimates separate models for the young elderly (aged between 60 and 74) and the very old (aged 75 or above). It is observed that the effect of public transportation accessibility on elderly mobility varies among elderly subgroups: public transportation accessibility significantly affects the trip frequency of the young elderly, but not that of the very old. Finally, a multitude of policy measures with the aim of upgrading elderly mobility is proposed. [ABSTRACT FROM AUTHOR]

Development of trip generation models has been conducted mainly using the traditional Multiple Linear Regression approach, which sometimes might not necessarily result in appropriate models, especially with existence of many interrelated and complex relationships among several related socioeconomic variables. This study investigates the feasibility of using a relatively new approach, the Adaptive Neuro-Fuzzy Inference System, and compares the results with those using the traditional approach. This is conducted by developing a home-based general trip generation model for one of the Palestinian urban areas. The comparison between the two methods outcome and the associated validation results is done using the R-squared, RMSE, and MAE measures. The Adaptive Neuro-Fuzzy Inference System was found to be a useful tool and a promising technique for modelling household trip generation, which is shown to outperform the traditional approach, with more accurate results and closer predictions to actual values. Further exploration of the new approach in transportation studies is recommended. [ABSTRACT FROM AUTHOR]

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The circumstances of passengers' decisions and behaviours concerning non-commute urban travels in Eastern Europe is not well-studied; most of the studies on this topic was done on Western societies. This paper presents the results of a study on Cracow, Poland. This study is based on a survey in two neighbourhoods of different urban forms in Cracow, one with central structure with compact land use and the other a representative of socialist urban form with big residential blocks and no central local places. The survey was carried out from January to February 2019 with 426 inhabitants. The results of the Ordinary Least Square models reveal that age, daily activity, the place of shopping, frequency of shopping in the vicinity of homes, and frequency shopping activities outside in farther places are significantly correlated with the frequency of non-work trips in Cracow. The sprawled decentral district produces a high correlation between shopping trips outside the district and the overall nonwork trip frequency, referring to the failure of the socialist urban form to keep non-work trips inside the districts by the presence of local facilities like shops and retail. It is discussed in this paper that such correlations may be very much context-specific, as there are some differences between the findings of this paper and those of international findings in high-income and developing countries. [ABSTRACT FROM AUTHOR]