Coordination and Diversification of Investments in Multimodal Transportation

Lambert, James H.; Joshi, Nilesh N.; Peterson, Kenneth D.; Wadie, Shadi M

doi:10.1177/1087724x07299629

Cited by 15 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can raise a validity issue of the final results due to the considerable influence of the pandemic on the transportation sector in the U.S. [41][42][43][44]. Additionally, according to Census Bureau [45], the pandemic adversely influenced the data collection process and may produce quality issues. Specifically, the sample obtained may not represent the entire population in the U.S. because the final number of interviews significantly reduced in 2020 [46].…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Exploring Associations between Multimodality and Built Environment Characteristics in the U.S

Lee

2022

Sustainability

View full text Add to dashboard Cite

This study demonstrated associations between multimodality and built environment characteristics, and proposed policy implications for fostering multimodal travel behaviors. It conducted a U.S. nationwide analysis using ordinary least square regression and gradient boosting decision tree regressor models with American Community Survey 2015–2019 5-year estimates and the United States Environmental Protection Agency Smart Location Database version 3.0. Notable findings were as follows: First, built environment characteristics were found to be statistically significant predictors of multimodality across the U.S. Second, certain features were identified as having considerable importance, specifically including population density, regional accessibility, walkability index, and network density, all of which should be given particular attention by transportation and land-use planners. Third, the non-linear effects of built environment characteristics on multimodality suggested an effective range to encourage multimodal transportation choice behaviors in various situations. The findings can guide the development of effective strategies to transform the built environment, which may subsequently be used to minimize reliance on automobiles and promote people to travel more sustainably.

show abstract

Section: Study Area and Data Collectionmentioning

confidence: 99%

Exploring Associations between Multimodality and Built Environment Characteristics in the U.S

Lee

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Finally, governance principles are necessary to guarantee an effective multimodal coordination of efforts among the public transport operators, as well as between operators, city Councils and authorities [3,35]. To this end, multimodal patterns can be seen as an objective and transparent ground to facilitate cross-carrier planning and explore route-and-schedule synergies for the benefit of the citizens.…”

Section: Challenges To Multimodal Traffic Data Analysismentioning

confidence: 99%

Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

Lemonde

Arsénio²,

Henriques

2021

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Worldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

show abstract

“…Several critical factors influence transportation planning and coordination of multimodal investments. Foremost, there is a need for increased coordination in resource allocation for multimodal transportation investments (Baker & Lambert, 2001; Lambert, Baker, & Peterson, 2003; Lambert, Joshi, Peterson, & Wadie, 2007; Lambert, Peterson, & Joshi, 2006). Coordination entails a technique or method for enhanced resource management (Lufkin, Desai, & Janke, 2005; Peters & Hidalgo, 1997), often resulting from teamwork of different agencies and backgrounds (Burkhardt, 2004).…”

Section: Review Of Relevant Literature and Practicesmentioning

confidence: 99%

Uncertain Cost Estimates Influencing Priorities for Multimodal Transportation Corridors

Lambert

Soliman

Linthicum

et al. 2013

Public Works Management & Policy

Self Cite

View full text Add to dashboard Cite

Economic uncertainties have prompted transportation departments to consider cost-effective spending to reduce funding shortages. Although methods exist for identifying and prioritizing projects, there is need for analysis of long-range plans that consider stakeholder perspectives. This article compares two pairs of cost estimates: (a) modal agency cost estimates with (b) highway-only implementations and (c) regional Transportation Planning Organization (TPO) plans with (d) the state Department of Transportation (DOT) highway plan. This article develops methods to compare the perspectives of cost estimates for transportation improvements. The methods are adaptable to include both preliminary cost data and detailed life cycle cost data. The results aid in the prioritization of engineering and long-range planning efforts for projects that are expected to contribute to the goals of a variety of stakeholder investments. The methods support efforts for increasing accuracy and inclusion of life cycle costs into project estimates and employment of more sophisticated cost methodologies, such as cost-effectiveness and cost–benefit analysis.

show abstract

Coordination and Diversification of Investments in Multimodal Transportation

Cited by 15 publications

References 10 publications

Exploring Associations between Multimodality and Built Environment Characteristics in the U.S

Exploring Associations between Multimodality and Built Environment Characteristics in the U.S

Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

Uncertain Cost Estimates Influencing Priorities for Multimodal Transportation Corridors

Contact Info

Product

Resources

About