2019

Chris De Gruyter, RMIT University

Trip generation estimates are integral to assessing the transport impacts of land use developments. However, past efforts have predominantely focused on vehicle trips only. This paper provides an international literature review of multi-modal trip generation associated with land use developments. A total of 153 publications were sourced as relevant to the review. The results show that while multi-modal trip generation studies have been relatively scant, they have received greater attention in the last 10 years. A range of issues were identified with estimating and applying multi-modal trip generation rates, not least was a lack of sufficient data and higher complexity in data collection compared to vehicle trip generation studies. Current knowledge gaps highlight opportunities to move towards greater international coordination and sharing of multi-modal trip generation data, along with exploring the use of technology to assist with data collection. Key directions for the future include a fundamental change in paradigm to consistently account for multi-modal trip generation, the development of an international multi-modal trip generation database, and greater sensitivity testing in assessing the multi-modal impacts of new land use developments.

Jinping Guan, Tongji University
Kai Zhang

Old downtown areas of Chinese megacities are being reformed and substandard housing is demolished. Government relocates residents to affordable city-peripheral large-scale residential areas. These residents are called “relocatees”. So far, very few studies have worked on the change of travel equity over time for relocatees’ and non-relocatees’ shopping-related trips in these areas in rapid-developing countries. To fill this gap, this study conducts two surveys (before and after Wanda Shopping Plaza’s opening), uses statistical analysis to understand shopping destination change, travel time, and travel mode share of relocatees and non-relocatees, and estimates travel mode choice logit models to calculate consumer surplus as a measurement of travel equity. Results show that ①Average travel time of relocatees is always significantly greater than that of non-relocatees, no matter for all trips or shopping-related trips. Megacity-periphery development and relocation have a more negative effect on relocatee’s travel than on other population; ② After Wanda’s opening, residents’ value of time increases from 0.41 CNY/min to 0.51 CNY/min; ③ Relocatee always has a lower travel quality for shopping-related trips no matter before or after Wanda’s opening. Travel equity of relocatees and non-relocatees for shopping related trips becomes worse after Wanda’s opening. This implies with economic growth and peripheral area’s development, the travel equity of relocatees and non-relocatees may become worse rather than better over time. This is worth considering by policy makers. The magnitude difference of consumer surplus between relocatees and non-relocatees provide a reference for the travel subsidy if the government plans to give one.

Yuichiro Kaneko, Nihon University
Takuro Nakagawa, CTI Engineering Co., Ltd.
Kheang Veng Phun, Japan Transport Research Institute
Hironori Kato, University of Tokyo

This study empirically analyzes the effects of urban railway investment on the regional population density, employment density, and land price using the spatial difference-in- differences (DID) approach that employs the sociodemographic and socioeconomic dataset in 2,843 zones in the Tokyo Metropolitan Area from 2000 to 2010. A spatial-lag model and a spatial-error model, in addition to an ordinary least square model under the framework of DID approach, are employed in the empirical analyses. The results show that urban railway lines were invested in areas with lower population densities and higher employment densities. The urban railway investment significantly positively influenced the land price but insignificantly influenced the population and employment densities. The land price was positively influenced by the population and employment densities. The analysis suggests that introduction of the railway directly affected the land price via anticipation of expected future development, rather than an indirect effect via increased population and employment densities. Finally, we discuss the policy implications to transit-oriented development, including a strategic residential development, in line with the railway investment and the integrated development of business clusters following the railway investment to enhance the economic effects of the railway investments.

Amy Smith, Uber Technologies, Inc.
Allison Wylie, Uber Technologies, Inc.
Andrew Salzberg, Uber Technologies, Inc.
Eric Womeldorff, Fehr & Peers
Geoff Rubendall, Fehr & Peers
Ingrid Ballus-Armet, Fehr & Peers

The increasing adoption of ridesourcing services has created new demands for curb space, spurring many cities to consider new approaches to curb space management. Some cities are going beyond considering changes, to experimenting with curb space regulation and physical infrastructure changes to accommodate the growth of shared mobility. Efforts to modify how streets are designed and managed can benefit greatly from a foundational understanding of existing conditions; and while existing conditions assessments are common practice for street design efforts, the introduction and growth of a new means of access to the curb, such as ridesourcing, creates a need to revisit and build upon how this space is traditionally considered and allocated. This paper presents a data framework for existing conditions assessments that includes the following four components: 1) City priorities, 2) Street design and land use, 3) Transportation characteristics, and 4) Mobility metrics. Using a combination of traditional data collection methods, such as video observation and traffic counts, as well as new methods for assessing ridesourcing activity, the framework was applied toward an existing conditions assessment of five locations in San Francisco. The assessment informed the development of recommendations for physically reconfiguring, reallocating, or re-envisioning curb space to promote increased mobility for a variety of roadway users, including ridesourcing passengers and drivers. The objective of this paper is to contribute case study examples that can be shared with public agency decision makers when contemplating how to re-envision their curb space in light of its evolving demands.

Ping Zhang, Tongji University
Zheyi Zhong, Tongji University

This study examines the various factors, including build environment, station characteristics, socio-economic and demographic characteristics and the competition of traditional transit, how effect the attraction of the urban rail transit. Data is analyzed from about 260 rail stations of Shanghai, China. Based on the traditional influence factors of resident travel, this study takes advantage of the POI data for a better description of the station environment. Because of the complexity and internal correlation of the various variables, factor analysis is used for reducing dimension, and 7 factors that are not interfere with each other are obtained. By using the Shanghai Metro as case studies, factors which are expected to contribute to higher rail transit usage are analyzed by using multiple regressions. The results show higher development intensity, higher commercialization level, more operation years of station, major interchange station and large traffic hub may be associated with increasing rail passenger flow.

Chester Fung, ARUP
Tam Tran, San Francisco Planning Department
Nicole-Anne Boyer, Adaptive Edge Consulting
Doug Johnson, San Francisco Planning Department
Linda Meckel, San Francisco County Transportation Authority
Grahm Satterwhite, San Francisco Municipal Transportation Agency

The current time is one of dramatic upheaval in the transportation field – radical technological and societal changes, some of which are already upon us, hold the potential to transform how people live and get around within cities. In this uncertainty, traditional transportation planning processes are falling short of providing the insight that agencies and communities need in order to respond appropriately to these changes. The single-point forecasting method used by traditional processes, that simply extrapolate conditions from existing to future, runs the risk of completely missing the future effects of forces that are not yet in play. Scenario planning is a different approach for thinking about the future, bringing techniques that enable planners to consider multiple potential futures, not just one, and therefore creating opportunities to factor future uncertainties into plans. ConnectSF, a 50-year transportation visioning process conducted by San Francisco agencies and stakeholders, is a recent application of scenario planning in the transportation context. This paper describes the ConnectSF process, showing how it considered external forces that might shape different future scenarios, explored the benefits and drawbacks of those scenarios, then provided a framework for identifying steps toward minimizing the undesirable consequences and capitalizing on the desirable features of the defined futures. The paper concludes with ideas on needed steps for better integrating scenario planning approaches into traditional planning processes.

Hannah Younes, University of Maryland, College Park
Arefeh Nasri, University of Maryland, College Park
Giovanni Baiocchi, University of Maryland, College Park
lei zhang, University of Maryland

Transportation disruptions offer opportunities to study how people adapt to using new modes of transportation and have important implications for transportation policy and planning. Bikeshare has emerged as a new popular mode of transportation in recent years as it offers an efficient and reliable way to travel short distances, and for its convenience as a first- and last-mile mode to complement transit. It also offers many social, environmental, and health-related benefits and has the potential to promote low-carbon mobility. This study examines changes in bikeshare ridership due to rail transit closures in the Washington, D.C. area and investigates how promoting bikeshare systems in large metropolitan areas could be beneficial in cases of transit disruptions – regardless of the type, cause, and duration. We use disaggregate trip history data to analyze the impact of three different transit closures in 2016 lasting 7 to 25 days. The objective of this paper is to provide insight on how transit disruptions affect bikeshare use. Kernel density estimation is applied to understand the changes in ridership from a week before, one year before, and after each closure. Results are compared both temporally and spatially and confirm that transit disruptions were associated with increased bikeshare ridership at the local level. Once the affected Metro stations reopened, bikeshare ridership returned to original levels. We conclude that when within 0.25 mile of a rail station and with a rail station spacing of less than 3 miles, bikeshare can be used as a mechanism for low-carbon mobility to complement transit.

David Duran Rodas, Technical University of Munich
Emmanouil Chaniotakis, Technical University of Munich
Constantinos Antoniou, Technical University of Munich

Bike sharing has been found to present environmental, economic and social benefits. Identification of factors influencing ridership is necessary for policy-making, as well as when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data-set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the models' estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also, on an international level in three cities in Europe and North America. The results suggest that the models' performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g. nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments and transport related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.

Michael Graehler, Jr., University of Kentucky
Richard Mucci, Kentucky Transportation Cabinet
Gregory Erhardt, University of Kentucky

This study aimed to replicate and update recently published research that conducted a longitudinal analysis of the determinants of public transit ridership in major North American cities. In doing so, it extended the longitudinal analysis to cover the period from 2015-2018 when notable declines in public transit ridership are observed. It also segments the models by mode to capture differing effects on rail versus bus. Our research suggests that their conclusion that “the reduction in bus VRK likely explains the reduction in ridership observe in recent years in many North American cities” may be flawed. While we do find that VRM is an important determinant of transit ridership, we also find it to be insufficient to explain the recent ridership declines, particularly the decline in ridership per VRM observed since 2015 for both bus and rail modes. Our results show that the introduction of bike share in a city is associated with light and heavy rail ridership, but a 1.8% decrease in bus ridership. Our results also suggest that for each year after TNCs enter a market, heavy rail ridership can be expected to decrease by 1.3% and bus ridership can be expected to decrease by 1.7%. It appears that TNCs may be a more important driver of recent ridership declines.

Hongliang Wan, Tongji University
Xiaohong Chen, Tongji University
Yi Lin, Sichuan University
Bin Ran, University of Wisconsin, Madison

Land-use configurations are known to have wide-ranging implications on the urban development including the transit system. This paper particularly focus on the complex relationship between land-use and metro ridership, and aim to offer an approach to this problem by using land-use features at two different levels of geo-information granularity: macro-level land-use categories and micro-level points of interest (POI) to evaluate their impact on metro ridership. In order to find the interdependencies, two different machine learning models were tested. It was found that both the decision tree and support vector regression achieve good predicting performance based on two criteria: predictive accuracy and area under the curve. Results also reveal that POI is the more influential feature variable when it comes to predicting metro ridership. Moreover, this paper also suggests a critical range for land-use to influence the metro ridership of a locality based on comparative analysis. Specifically, a radius of 0.25 km around the metro stations in city centre, a radius of 0.5 km in urban area and a radius of 1 km in suburb should be taken as the key issues of interest in land-use planning. The paper can help shed light on underlying relationship between metro ridership and land-use variables, and can be used to developing the transit-oriented development plans to improve effectiveness and efficiency of mass rapid transit by improving utilization of the urban facility POIs around metro station.

Punith B Kotagi, National Institute of Technology Karnataka
Gowri Asaithambi, National Institute of Technology, Karnataka

Traffic in developing and emerging countries is mixed in nature with widely varying types of vehicle with absence of lane discipline. Private vehicles such as two-wheelers and cars occupy the dominant share in vehicular composition in most of the urban cities. Increase in composition of these private vehicles is the major cause for traffic congestion and emissions during peak hours. Traffic simulation models play an important role in effectively addressing these problems through better management and operation strategies. One of the feasible solutions to reduce congestion and emission in developing cities is to promote usage of public transportation system. Hence, the present study aims to evaluate the impact of modal shift from private vehicles (two-wheelers and cars) towards public transport (buses) on capacity, travel time and emissions using simulation model. For this purpose, a microscopic simulation model for urban undivided roads in mixed traffic is developed using Object Oriented Programming (OOP) concepts and implemented in MATLAB programming language. The modal shift analysis is carried out using the simulation model for three study locations located in Bangalore, Delhi and Mumbai cities in India. The optimum number of buses to be increased on each study section is obtained by carrying out scenario analysis. The comparative analysis of scenarios, before and after modal shift of private vehicles to public transport suggests that an increase in the number of buses has significant improvement in capacity, travel time and traffic emissions