Networking Transportation

Networking Transportation looks at how the digital revolution is changing Greater Philadelphia's transportation system. It recognizes several key digital transportation technologies: Artificial Intelligence, Big Data, connected and automated vehicles, digital mapping, Intelligent Transportation Systems, the Internet of Things, smart cities, real-time information, transportation network companies (TNCs), unmanned aerial systems, and virtual communications. It focuses particularly on key issues surrounding TNCs. It identifies TNCs currently operating in Greater Philadelphia and reviews some of the more innovative services around the world. It presents four alternative future scenarios for their growth: Filling a Niche, A Tale of Two Regions, TNCs Take Off, and Moore Growth. It then creates a future vision for an integrated, multimodal transportation network and identifies infrastructure needs, institutional reforms, and regulatory recommendations intended to help bring about this vision

Governance and Regulation of Ride-hailing Services in Emerging Markets: Challenges, Experiences and Implications

This paper seeks to shed some light on the different considerations for regulation and governance of ride-hailing platforms in emerging markets, highlighting their positive and negative externalities. Building on an extensive review of the literature and secondary sources, we outline Ride-hailing's identified and potential effects on users (providers and consumers), incumbents, and society. Based on the welfare impacts structure, we identify the significant challenges that regulators face in understanding, monitoring, evaluating, and regulating this type of transportation innovation. Finally, the paper proposes a framework for approaching such mobility innovations from governance and regulation perspectives. In a context of exponential growth in research and innovation in urban mobility in general and Ride-hailing, a rigorous review of the literature and a critical framework for understanding governance and regulation in such services in rapidly changing contexts is a timely contribution

Investigating Mode Choice of Ridesourcing Services: Accounting for Attitudes and Market Segmentation

The phenomenal development of ridesourcing is possibly one of the greatest revolutions that have happened to transportation networks. Ridesourcing improves mobility and mitigates traffic congestion by reducing vehicle ownership and serving as a first/last-mile feeder to public transportation. This tremendous growth created a burgeoning literature exploring ridesourcing users\u27 characteristics, yet there is no clear picture of its market. In the absence of sufficient information, policymakers face a major challenge in planning equitable and accessible transportation systems. This dissertation presents a detailed analysis of individuals’ decisions to adopt ridesourcing, focusing on three main objectives that have not been addressed previously. First, a reduced fare of ridesourcing was considered to explore its adoption beyond cost constraints. Second, the effect of attitudes on the choice of ridesourcing was explored. Lastly, the adoption of ridesourcing across various market segments was examined. Advanced economic models were applied to the data from a stated preference survey, which is a rich database of attitudes and mobility patterns. The results indicate that attitudes play a major role in the adoption of ridesourcing and considering the impact of attitudinal factors could provide valuable insights into individuals’ behavior toward ridesourcing. It was shown that attitudinal factors (e.g., technology-savviness, driving enjoyment) could explain individuals\u27 choice behavior in a way that cannot be clarified by socioeconomic and demographic factors. The market segment-based analysis of ridesourcing adoption demonstrated that different segments have distinct perceptions and attitudes toward ridesourcing. For instance, for regular transit users, travel time and cost perceptions are decisive factors in adopting ridesourcing. In contrast, visitors (i.e., auto users when their vehicle is unavailable) will adopt ridesourcing when it provides higher utility regarding time, cost, and convenience. Moreover, regarding the impact of ridesourcing experience on the adoption of these services, it was shown that individuals with no ridesourcing experience are more sensitive to traveling with strangers, worry about the higher travel time, and are more attached to their vehicles. Finally, considering the role of generational effects on ridesourcing adoption, it was shown that Generation Xers\u27 choice highly depends on the perceived utility of shared mobility and their desires for mobility for non-drivers features. Contrarily, Millennials’ choices are more likely to be affected by their preference toward technology and driving stress relief

The use of Uberpool and its Relationship with Public Transport -A London case study

The growth of novel forms of shared and non-shared ridesourcing services such as Uberpool and UberX, are nowadays a common feature of transport options in many cities globally (i.e., London). The rapid growth of these new services is creating challenges and opportunities for transport authorities and policymakers who so far have been slow to respond to policy and operational demands. There has been much publicity about the possible effects of Uber services in London and ongoing debates among the transport authorities and other key stakeholders on how or if these services should be managed or regulated. However, with the absence of empirical data and a clear understanding of the current and future implications for traditional PT modes, the consequences of ridesourcing services on London’s transport system are not evident. This study provides insights about the usage and user characteristics of ridesourcing services and how such services work with PT modes and explores the implications of Uberpool on conventional PT in terms of policy and operations. The literature review for this study revealed that empirical research on shared ridesourcing has been limited, mostly because of limited data availability and as a result, effects on other modes such as PT are less understood. Current literature indicates some of the key factors in ridesourcing adoption, include the socio-demographic of users, convenience, cost, and general changing attitudes towards sharing. The current literature on ridesourcing shows that most of the existing research the topic was primarily undertaken in a North American (i.e., the USA) perspective and the findings do not fully capture the policy, and operational issues that relate specifically to a European or UK context. Furthermore, shared ridesourcing is not adequately addressed in the current literature, particularly its impact and relationship with PT services. As such, it is not fully understood, and there is no consensus on how transport authorities and policymakers should deal with these new services. In addition, previous research mainly used a singular approach or considered only one stakeholder (i.e., the users or drivers) and thus did not fully consider the perspectives from all interested parties such as the users, drivers, service providers (TNCs), policymakers and transport authorities and other transport mode operators. To achieve the study objectives and address the research gaps, the following three primary research questions were established. 1. How are UberX and Uberpool currently used in a city like London? 2. What attracts people to Uberpool in a city like London? 3. How do transport authorities and the conventional public transport industry deal with Uberpool in a city like London? For this study, a mixed-methods approach involving the collection of quantitative and qualitative data was used. The quantitative data were collected using a survey of UberX and Uberpool users in London, which yielded 907 responses. The qualitative data were collected using a combination of interviews with 31 different transport policymakers, PT operators and other key stakeholders and focus groups with 28 London Uber drivers. The interview and focus group data were analysed using a thematic approach to find meaningful themes in the data. The survey data was initially analysed using descriptive statistical analysis and cross-tabulation. Moreover, several categorical regression (CATREG) models were developed for the survey data to investigate a greater understanding of the key factors that influenced how and why Uberpool services were used in London. The results indicated that most Uberpool users in London were employed (77.4% of respondents) and educated to degree level (89.5%), with 60% of respondents using PT (i.e., Buses, Trains/Tube) for same or similar trips before Uber and 49.9% of trip purposes were going to “work, college/school, or PT station/stop”. The key factors which influenced a passengers’ decision to use Uberpool instead of PT modes included “perception on safety, compared to PT modes”, “employment status”, “age group”, “trip purpose”, and “car ownership at present”. The results revealed that Uberpool was popular with students, travellers making social (i.e., night out) or long-distance trips. The findings highlight that transport authorities were currently poorly equipped (for various reasons) to deal with these new on-demand services, and there was a need to develop specific transport policy measures and regulations for ridesourcing services which considered input from all key stakeholders, including service providers, PT operators, the users, and ridesourcing drivers. At the time of completion, this was the first study in the UK that used empirical data collected from key stakeholders (i.e., users, Uber drivers and policymakers) to investigate how shared (Uberpool) and non-shared (UberX) ridesourcing services are used and its relationship with traditional PT modes. The findings present important insights into the implications of ridesourcing services for traditional PT, active mode, and the influencing factors on why users adopt ridesourcing instead of other modes and the findings can support policymakers and transport authorities during policy and regulation development. In this study, several key recommendations are offered, including the need to integrate ridesourcing services with other modes of transport in London (e.g., the PT) and providing guidance to ridesourcing and PT operators on how best these services should be integrated (e.g., payment systems) to complement one another and reduce negative impacts the city’s PT network. Furthermore, suggestions on ridesourcing data collection and monitoring methods are presented to address the lack of ridesourcing data, which remains a significant issue in London. In addition, suggestions are made for developing specific regulations for ridesourcing, since there are currently no specific regulations covering ridesourcing in London, and these services are operating under the PHV regulations, which was not developed for these types of services and thus did not address the challenges brought forth by shared and non-shared ridesourcing services. The development of new ridesourcing regulations should involve consultations with all key stakeholders and should aim to maximise the opportunities offered by ridesourcing services whilst addressing the existing regulatory gaps in the taxi and PHV legislation, including driver standards, welfare (i.e., maximum working hours and sick pay) and defining clear responsibilities for all those who are involved in providing ridesourcing. Considering this study’s scope, several opportunities for future research are identified, including future research to understand inequalities in accessing and using ridesourcing services, particularly for the elderly and those who do not have access to the internet or smartphones. Moreover, additional studies are suggested to clarify the role of Uberpool services in fulfilling first and last-mile trips, including how often PT passengers used shared ridesourcing to connect to/from PT modes (i.e., the tube, trains, or buses). Further research is recommended to investigate the broader effects of all the different ridesourcing services on London’s traffic congestion and the wider economic implications from these services, including benefits, disbenefits and the total costs of these services for the city, users, and the drivers

Shifting from Driving to Riding: a study of the impacts of on-demand cab services on public transit ridership and vehicle ownership in Hyderabad, India

This thesis explores the effects of on-demand cab services, Uber and Ola, on public transit ridership and vehicle ownership in Hyderabad, India. India has grown to be Uber’s third largest market in the world but still lacks any comprehensive policies at the federal level to regulate on-demand cab services. These services have risen in popularity and have led to the evolution of new ownership and financial models to help populations afford a car to “drive to work.” This research examines the spatial effects of this rising popularity on public transit ridership and vehicle ownership in Hyderabad. This research found that there has been a shift in the proportion of on-demand cabs and cars to all vehicles from 2010 to 2016. This research also found that annual occupancy ratio along bus routes in the city has decreased from 2014 to 2016. This research found that these relationships are localized in the city. This thesis concludes by recommending further studies be carried out to understand the full extent of these effects to effectively incorporate these technologies and plan for the future mobility of city residents

Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for all trips made within the city since November 1, 2018. The data comprises the trip ends (pick-up and drop-off locations), trip timestamps, trip length and duration, fare including tipping amounts, and whether the trip was authorized to be shared (pooled) with another passenger or not. Therefore, the main goal of this dissertation is to develop a comprehensive data-driven framework to understand and model the system using this data from Chicago, in a reproducible and transferable fashion. Using data fusion approach, sociodemographic, economic, parking supply, transit availability and accessibility, built environment and crime data are collected from open sources to develop this framework. The framework is predicated on three pillars of analytics: (1) explorative and descriptive analytics, (2) diagnostic analytics, and (3) predictive analytics. The dissertation research framework also provides a guide on the key spatial and behavioral explanatory variables shaping the utility of the mode, driving the demand, and governing the interdependencies between the demand’s willingness to share and surge price. Thus, the key findings can be readily challenged, verified, and utilized in different geographies. In the explorative and descriptive analytics, the ride-sourcing system’s spatial and temporal dimensions of the system are analyzed to achieve two objectives: (1) explore, reveal, and assess the significance of spatial effects, i.e., spatial dependence and heterogeneity, in the system behavior, and (2) develop a behavioral market segmentation and trend mining of the willingness to share. This is linked to the diagnostic analytics layer, as the revealed spatial effects motivates the adoption of spatial econometric models to analytically identify the ride-sourcing system determinants. Multiple linear regression (MLR) is used as a benchmark model against spatial error model (SEM), spatially lagged X (SLX) model, and geographically weighted regression (GWR) model. Two innovative modeling constructs are introduced deal with the ride-sourcing system’s spatial effects and multicollinearity: (1) Calibrated Spatially Lagged X Ridge Model (CSLXR) and Calibrated Geographically Weighted Ridge Regression (CGWRR) in the diagnostic analytics layer. The identified determinants in the diagnostic analytics layer are then fed into the predictive analytics one to develop an interpretable machine learning (ML) modeling framework. The system’s annual average weekday origin-destination (AAWD OD) flow is modeled using the following state-of-the-art ML models: (1) Multilayer Perceptron (MLP) Regression, (2) Support Vector Machines Regression (SVR), and (3) Tree-based ensemble learning methods, i.e., Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost). The innovative modeling construct of CGWRR developed in the diagnostic analytics is then validated in a predictive context and is found to outperform the state-of-the-art ML models in terms of testing score of 0.914, in comparison to 0.906 for XGBoost, 0.84 for RFR, 0.89 for SVR, and 0.86 for MLP. The CGWRR exhibits outperformance as well in terms of the root mean squared error (RMSE) and mean average error (MAE). The findings of this dissertation partially bridge the gap between the practice and the research on ride-sourcing transportation systems understanding and integration. The empirical findings made in the descriptive and explorative analytics can be further utilized by regional agencies to fill practice and policymaking gaps on regulating ride-sourcing services using corridor or cordon toll, optimally allocating standing areas to minimize deadheading, especially during off-peak periods, and promoting the ride-share willingness in disadvantage communities. The CGWRR provides a reliable modeling and simulation tool to researchers and practitioners to integrate the ride-sourcing system in multimodal transportation modeling frameworks, simulation testbed for testing long-range impacts of policies on ride-sourcing, like improved transit supply, congestions pricing, or increased parking rates, and to plan ahead for similar futuristic transportation modes, like the shared autonomous vehicles

Comparing different transit strategies to tackle the last-mile issue in low demand areas Case study: York Region Transit

Providing public transit service in low-density suburban areas is very challenging and inefficient because development patterns and transit demand do not support regular scheduled bus services while flexible and on-demand service is very expensive to provide. A further issue is that effective public transit is essential for providing equal access to opportunities for the residents of these areas. This is a controversial issue in most Canadian cities where they have difficulties in providing sustainable public transit. Building upon the knowledge gained from an overview of the literature, this study aims at contributing to a better understanding of the crucial factors that influence the performance of public transit in low-density areas and develops a framework for evaluating different strategies for providing first/last mile transit service. In order to accomplish this goal the literature of transit system performance measures as well as transit mode choice are reviewed and 7 major criteria are selected: safety & security, cost, time, flexibility, comfort, coverage, and availability of information. Secondly, a systematic literature review is conducted to identify different strategies that can be implemented as a last mile solution in low density areas. Employing the seven criteria in an evaluation framework, these possible strategies are explained and compared. A case study using real data from York Region Transit (YRT) were utilized for comparing the two most common on-demand last mile strategies in the region. Results showed that outsourcing transit rides to instant ride-hailing companies –e.g. Uber- is financially beneficial to YRT and offers more coverage for potential riders, providing that reliability of their service is secured

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions