Estimating the potential demand for Demand-Responsive Transport based on smartcard transactions

The future of Demand-Responsive Transport (DRT) is investigated in respect to the potential travelers' demand. The potential demand for the DRT service is studied based on a database of 63 million records of the public transportation (PT) trips made in Israel using a smartcard in June, 2019. Our major assumption is that travelers may prefer a DRT service over conventional PT for making a non-routine trip that occurs only once a month. The behavioral patterns of PT users were revealed by clustering their boarding records based on the location of the boarding stops and the boarding time of day using an extended DBSCAN algorithm. We make three major discoveries: at least 30 percent of the PT trips do not belong to any cluster of monthly user activity; conventional home-work-home commuters are a minority and constitute less than 15 percent of the drivers; the vast majority of the users make, during the month, both recurrent and occasional trips. The share of occasional trips is 25 percent for frequent users who make over 40 trips a month, and as high as 60 percent for those who board PT up to 10 times a month. We uncover the dependencies of trip regularity on population group, time of day and land use composition around the location of trip origin. We conclude that in high-density urban areas, conventional PT may lose substantial ridership to DRT. The spillover to DRT may be prevented by improving the level of service and incentivizing conventional PT users. We discuss using our approach to identify city areas and PT lines where occasional ridership is common, and user groups that are more likely to switch from conventional PT to DRT

A greedy approach for increased vehicle utilization in ridesharing networks

In recent years, ridesharing platforms have become a prominent mode of transportation for the residents of urban areas. As a fundamental problem, route recommendation for these platforms is vital for their sustenance. The works done in this direction have recommended routes with higher passenger demand. Despite the existing works, statistics have suggested that these services cause increased greenhouse emissions compared to private vehicles as they roam around in search of riders. This analysis provides finer details regarding the functionality of ridesharing systems and it reveals that in the face of their boom, they have not utilized the vehicle capacity efficiently. We propose to overcome the above limitations and recommend routes that will fetch multiple passengers simultaneously which will result in increased vehicle utilization and thereby decrease the effect of these systems on the environment. As route recommendation is NP-hard, we propose a k-hop-based sliding window approximation algorithm that reduces the search space from entire road network to a window. We further demonstrate that maximizing expected demand is submodular and greedy algorithms can be used to optimize our objective function within a window. We evaluate our proposed model on real-world datasets and experimental results demonstrate superior performance by our proposed model

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Eco-navegação para uma mobilidade elétrica, autónoma e partilhada

Atualmente, as emissões provenientes do sector do transporte representa uma das principais fontes emissoras de poluentes para a atmosfera trazendo consequências graves para o nı́vel da qualidade do ar e da saúde pública. Esta dissertação de mestrado tem como objetivo estudar e avaliar a penetração de veı́culos partilhados, elétricos e autónomos no desempenho no tráfego, utilizando um modelo macroscópico. Foram realizados vários cenários com diferentes penetrações de veı́culos partilhados, elétricos e autónomos, de forma a estudar o impacto das emissões de CO2 e NOx em termos de emissões por sistema e por veı́culo, no sentido Coimbra a Aveiro, através de 2 rotas (A1 e N235/IC2). Além dos vários cenários simulados no VISUM uma metodologia de cálculo de emissão de poluentes para dois veı́culos convencionais português a diesel e gasolina foi usada, assim como uma equação que relaciona a velocidade com o consumo, para um VE. A validação do modelo de tráfego consistiu na recolha de dados dos planos intermunicipais de mobilidade de Aveiro e Coimbra, de volumes de tráfego do IMT, e tempos de viagem indicativos. Os resultados sugerem que para um conjunto de combinações realistas de cenários de penetração de mobilidade alternativa, as emissões do sistema mostram uma redução de 60% a um aumento de 16% para os nı́veis de CO2, e uma redução de 99% a um aumento de 16% para os nı́veis de NOx. As emissões por veı́culo variam num intervalo de -50% a +1% e -54% a +6% para os nı́veis de CO2 e NOx respetivamente. Assim o cenário 12 com uma penetração de 33% veı́culos partilhados e 18% de veı́culos elétricos, é o cenário com a maior diminuição quando é tido em conta as emissões no sistema, e o cenário 13 em termos de emissões por veı́culo que além de conter uma penetração de 33% veı́culos partilhados e 18% veı́culos elétricos, contém 35% de veı́culos autónomos quando ambas as rotas são consideradas. Além disto foram analisados pontos crı́ticos em ambas as rotas para o cenário base, que demonstraram ser semelhantes. Estes pontos crı́ticos com altos nı́veis de emissões mostraram uma relação com a diminuição das velocidades dos veı́culos, assim como o aumento do volume de tráfego, e vice-versa.Currently, emissions from the transport sector represent one of the main sources of air pollutants to the atmosphere bringing serious consequences for air quality and public health. This masters dissertation aims to study and evaluate the performance in traffic using a macro simulation model. Several scenarios were assessed with different penetrations of shared, electric and autonomous vehicles, in order to study the impact on CO2 and NOx emissions in terms of system emissions and per vehicle, in the direction Coimbra to Aveiro, via 2 routes (A1 and N235/IC2). In addition to the various scenarios simulated in VISUM, a pollutant emission calculation methodology for two typical Portuguese diesel and petrol vehicles was used, as well as an equation that relates velocity with consumption for an EV. Validation of the traffic model, consisted in data of the Aveiro and Coimbra intercity mobility plans, IMT traffic volumes, and indicative travel times. The results suggest that for a group of realistic combinations of alternative mobility penetration scenarios, system emissions show a reduction from 60% to an increase of 16% for CO2 levels, and a reduction from 99% to an increase of 16% for NOx levels. Emissions per vehicle show a range from -50% to +1% and from -54% to +6% for CO2 and NOx levels respectively. Therefore scenario 12 with a 33% and 18% penetration of shared vehicles and electric vehicles respectively, is the scenario with the biggest decrease when considering system emissions, and scenario 13 in terms of emissions per vehicle that have besides these penetrations of SVs and EVs, a penetration of 35% autonomous vehicles, when both routes are taken into account. In addition, critical sectors were analyzed on both routes for the base scenario, which proved to be similar. These critical sectors with high emission levels showed a relationship with the decrease in vehicle speeds, as well as the increase in the traffic volume, and vice-versa.Mestrado em Engenharia Mecânic

Modeling Individual Activity and Mobility Behavior and Assessing Ridesharing Impacts Using Emerging Data Sources

Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as mobile phone and social media data, mobility data extracted of the new data sources contain more specific, detailed, and longitudinal information of individual travel mode and coordinates of the visited locations. This dissertation explores the potential of using GPS-based FCD and on-demand ride-hailing service data with different modeling techniques towards understanding and predicting individual mobility and activity behaviors and assessing the ridesharing impacts through three studies

Automated and electrified ride-hailing fleet: opportunities and management optimisation

This thesis explores key aspects and problems of technological innovations in the context of ride-hailing systems, shedding light on their profound implications for the industry. Chapter 2 introduces a centralised matching approach that integrates the EV charge scheduling problem into the optimisation framework of ride-hailing systems. The objective represents three-fold benefits: direct financial gains, service quality and system efficiency, and fleet profitability. Moreover, the chapter addresses the practical scenario where human drivers may reject charging assignments lacking personal incentives, leading to a driver compliance behavioural model and a corresponding incentivisation scheme. Chapter 3 introduces a macroscopic model underpinning demand-supply dynamics within mixed-fleet ride-hailing markets. Employing a model predictive control (MPC) framework, it optimises control variables to maximise operators' profits through dynamic trip fares for AVs and HVs, and the active AV fleet size. The study accounts for human driver work patterns and different exit behaviours. Leveraging historical data and real-time inputs, a comprehensive simulation testbed substantiates the efficacy of the proposed strategy in maximising operator profits while mitigating trip cancellations. Chapter 4 introduces a decentralised cooperative cruising approach for a-taxi fleet as an essential contingency plan during complete communication breakdowns. It quantifies road centralities using PageRank, serving as a measure for long-term passenger encounter likelihoods. This metric informs both cruising route planning and network partitioning for effective destination selection. Comparative analyses against benchmark strategies reveal significant enhancements in service performance across various fleet sizes. The research contributes comprehensive methodologies and insights, paving the way for more efficient, sustainable, and adaptable transportation systems

Power trip : smart management of shared autonomous electric vehicle fleets

The climate crisis requires substantial shifts in the transportation and energy sectors. Greater use of intermittent renewable energy sources requires demand- and supply-side flexibility in electricity markets. Deployment of on-demand, shared, fully automated, and electric vehicle (SAEV) fleets offers natural synergies in meeting such challenges. Smart charging (and discharging) of electric vehicles (EVs) can shift loads away from peak demand to reduce, or at least delay, expensive infrastructure upgrades, while fleet managers lower emissions and power costs in real time. This dissertation explores (1) optimization-based idle-vehicle dispatch strategies to improve SAEV fleet operations in the Austin metro, (2) integration of power and transportation system (EV-use) modeling across the Chicago metro area, and (3) a case study of demand response participation and charging station siting in a region with multiple energy suppliers. Optimizing SAEV repositioning and charging dispatch strategies jointly lowered rider wait times by 39%, on average, and increased daily trips served per SAEV by 28% (up to 6.4 additional riders), compared to separate range-agnostic repositioning and heuristic charging strategies. Joint strategies may also decrease the SAEV fleet’s empty travel by 5.7 to 12.8 percentage points (depending on geofencing and charging station density). If fleets pay dynamic electricity prices and wish to internalize their upstream charging emissions damages, a new multi-stage charging problem is required. A day-ahead energy transaction problem provides targets for a within-day idle-vehicle dispatch strategy that balances charging, discharging, repositioning, and maintenance decisions. This strategy allowed the Austin SAEV fleet to lower daily power costs (by 15.5% or $0.79/day/SAEV, on average) while reducing health damages from generation-related pollution (2.8$0.43/day/SAEV, on average). Fleet managers obtained higher profits ($8 per SAEV per day) by serving more passengers per day than with simpler (price-agnostic) dispatch strategies. This dissertation also coupled an agent-based travel demand simulator (POLARIS) with an electricity grid model (A-LEAF) to evaluate charging impacts on the power grid across seasons, household-EV adoption levels, SAEV mode shares, and dynamic ride-sharing assumptions in 2035 for the Chicago, Illinois metro. At relatively low EV penetration levels (8$100/MWh, or $0.10/kWh) and peak summer hours (+$300/MWh) due to higher energy fees and steep congestion fees on Illinois’ 2015-era transmission system. Although a smart-charging SAEV fleet uses wholesale prices to reduce electricity demand during peak hours, spreading charging demand in hours before and after the baseline peak creates new "ridges" in energy demand, which raise prices for all. These simulation results underscore the importance of investing in transmission system expansion and reducing barriers to upgrading or building new transmission infrastructure. If vehicles and chargers support bidirectional charging, SAEVs can improve grid reliability and resilience at critical times through demand response (DR) programs that allow load curtailment and vehicle-to-grid (V2G) power. Scenario testing of DR requests in Austin ranging from 1 MW to 12 MW between 4 and 5 PM reveals break-even compensation costs (to SAEV owners) that range from $86/kW to$4,160/kW (if the city imposes unoccupied travel fees), depending on vehicle locations and battery levels at the time of the DR request. Smaller requests can be met without V2G by reducing charging speeds, usually from 120 kW speed to Level 2 charging. Finally, an incremental charging station heuristic was designed to capture differences in land costs and electricity rate structures from different energy suppliers in the same region. The daily amortized costs over 10 years of hardware, installation, and land costs were estimated to be nearly $0.30/SAEV/day, compared to$0.38/SAEV/day with a baseline heuristic strategy ignoring land costs and marginal costs of expanding existing sites. SAEV charging costs showed no substantial difference between heuristic strategies, although combined daily energy fees were more expensive at $0.43/SAEV/day. Including land costs in charging station investment heuristics is necessary, and modelers should include spatially varying energy prices since the average daily per-vehicle energy costs are higher than the physical station costs. Taken together, this dissertation’s contributions offer hope for a decarbonizing world that provides affordable, clean, and convenient on-demand mobility.Civil, Architectural, and Environmental Engineerin

Shared ownership and ridership of driverless cars in Edinburgh

The research explores the attitudes towards sharing driverless cars (DC) among Edinburgh residents. DC Use is characterised by two dimensions: ownership and ridership. The personal mobility landscape has the potential to experience a paradigm shift over the coming decades due to the advent of level 5 DC, allowing people to enjoy hassle-free travel independent of the ability to drive. Therefore, DC use may generate more trips and miles travelled, aggravating further congestion and emissions, reducing the viability of traditional public transport services and people's propensity to walk and cycle. The shared use of DC could increase vehicle usage efficiency, make mobility more sustainable and affordable, and make cities more liveable.Existing research has investigated the impact of shared DC use on travel behaviour through simulations and choice experiments. These studies examined shared ownership and ridership separately. Few studies investigated the impact of travelling with family members and strangers of mobility choice; no attention is paid to household dynamics. To fill these gaps, the present study (a) identifies the propensities to share ownership and ridership of DC in different travelling scenarios; (b) analyses the impact of current travel behaviour socio-economic characteristics on such propensities; (c) jointly considers personality traits and social norms attitudes as factors explaining shared use of DC. The scenarios consider three shared DC ownership models (private DC, partially owned DC, driverless taxis) and three shared DC ridership models (riding alone, with close contacts, with strangers), with and without the presence of family members. Regular and occasional trips are investigated.Data is collected through an online questionnaire with 500 respondents, three-quarters of whom are of working age and owning a car. The questions are based on a literature review and interviews with mobility experts. Four areas are covered: current carsharing and ridesharing attitudes; determinants of attitudes towards carsharing and ridesharing; likelihood of adopting different DC ownership and ridership models; personality traits, social norms, and socio-demographic characteristics. Classes of carsharing and ridesharing behaviour are identified using cluster analyses. Discrete choice models are estimated to explain respondents' propensity for selected DC shared ownership and ridership scenarios, using the sharing behaviour, personality, social norms, and socio-demographic characteristics as determinants.Frequent household-car users are inclined to adopt private DC, whereas highly educated respondents older than 55 are less inclined to private DC. Higher-earners, younger-aged, cooperative and resource-sharing behaviour are significant determinants of driverless taxi use. City-centre dwelling, cooperative millennials are more willing to share DC with a stranger.People's reluctance to share trips with strangers is a crucial barrier to shared DC use. Privacy-preserving DC design can help people feel safer in sharing with a stranger. Public transport integration with DC should be investigated to promote further the shared use of DC