Simulating a rich ride-share mobility service using agent-based models

Transportation - Mobility as a Service (MaaS) is the integrated and on-demand offering of new mode-sharing transport schemes, such as ride-share, car-share or car-pooling. MaaS schemes may solve...     

Heterogeneity in marginal value of urban mobility: evidence from a large-scale household travel survey in the Greater Toronto and Hamilton Area

Transportation - The value of mobility is an unresolved question in transportation economics literature. The advent of ride-hailing services and the emergence of mobility as a service (MaaS) place...     

The potential of mobility as a service bundles as a mobility management tool

Transportation - The Mobility as a Service (MaaS) concept has recently taken the transport industry by storm. However, as applications and research on it are limited, there is still little evidence...     

Delivering mobility as a service (MaaS) through a broker/aggregator business model

Transportation - Mobility as a service (MaaS) promises a bold new future where bundled public transport and shared mobility options (carsharing, ridesharing, bikesharing and microtransit) will...     

Privacy-preserving MaaS fleet management

On-demand traffic fleet optimization requires operating Mobility as a Service (MaaS) companies such as Uber, Lyft to locally match the offer of available vehicles with their expected number of requests referred to as demand (as well as to take into account other constraints such as driver’s schedules and preferences). In the present article, we show that this problem can be encoded into a Constrained Integer Quadratic Program (CIQP) with block independent constraints that can then be relaxed in the form of a convex optimization program. We leverage this particular structure to yield a scalable distributed optimization algorithm corresponding to computing a gradient ascent in a dual space. This new framework does not require the drivers to share their availabilities with the operating company (as opposed to standard practice in today’s mobility as a service companies). The resulting parallel algorithm can run on a distributed smartphone based platform.     

Inviting travelers to the smorgasbord of sustainable urban transport: evidence from a MaaS field trial

A Mobility-as-a-Service (MaaS) concept, UbiGo, was implemented in Gothenburg, Sweden, and used for a 6-month period by 195 individuals in 83 households. Four participant subgroups were identified: Car shedders, Car accessors, Simplifiers, and Economizers. A qualitative analysis revealed that the subgroups had different reasons to join the service and different expectations of the change that would occur on the basis of the altered preconditions offered by the service. Previous car users reduced their use of private car and increased their use of public transport and active modes. Participants who did not have access to a privately-owned car but thought they needed one discovered that they managed well without. Other participants were reinforced in their existing behaviors but in ways they did not envisage, depending on which goals they had at the outset of the trial. Overall, the participants were also satisfied with the service, as well as with stated changes and non-changes, even if this in some cases meant more planning. Based on the empirical findings it could be argued that a service approach, such as UbiGo, has the potential to reduce the need for private car ownership, and enable people to change their mode choices and travel patterns. The potential relies however on a number of specific features of the service of which flexibility and a need- rather than a mode-based approach are key features.     

Policy implementation of multi-modal (shared) mobility: review of a supply-demand value proposition canvas

ABSTRACT

Urban mobility options have increased in recent years, assisted by the widespread availability of smart device software apps, geo-positioning technology, and convenient electronic financial transactions. Multi-modal shared mobility consists of public transit systems and shared mobilities that support first/last mile travel, denoting the capability of Mobility as a Service (MaaS), and to stimulate additional non-private car travel demand. This paper reviews the supply and demand sides of implementation of multi-modal shared mobility. It found that an abundance of shared modes of car, bike, and e-scooter that are linked to public transport, can improve transport accessibility to meet specific public preferences, reduce social inequality, and minimise dilemmas from the demand side. This study introduces government policy innovations and other supporting system to improve the implementation of multi-modal shared mobility. Government policies play a key role in supporting shared mobility and technology development. However, governments do not have much information about new products such as shared mobility, which creates difficulties in subsidising multi-modal shared mobility services and potentially leads to policy failures around shared mobility schemes. This study suggests that policy entrepreneurship in collaboration with other partners, policy innovation, and the notions of merit goods and second-best policymaking can enable policy initiatives towards multi-modal shared mobility and provide supporting arguments if policies encounter failures. Implementing multi-modal shared mobility requires a collaborative partnership for a paradigm shift: service providers and government must jointly set a merit-based business model, with the support of organisations to achieve improved infrastructure provision, and smart technology applications. The findings will assist the community, business providers and government policymakers to promote multi-modal shared mobility as a pathway towards more efficient, environmentally sustainable, and socially responsive mobility solutions.     

A framework for robustness analysis of road networks for short term variations in supply

There is a growing awareness that road networks, are becoming more and more vulnerable to unforeseen disturbances like incidents and that measures need to be taken in order to make road networks more robust. In order to do this the following questions need to be addressed: How is robustness defined? Against which disturbances should the network be made robust? Which factors determine the robustness of a road network? What is the relationship between robustness, travel times and travel time reliability? Which indicators can be used to quantify robustness? How can these indicators be computed? This paper addresses these questions by developing a consistent framework for robustness in which a definition, terms related to robustness, indicators and an evaluation method are included. By doing this, policy makers and transportation analyst are offered a framework to discuss issues that are related to road network robustness and vulnerability which goes beyond the disconnected definitions, indicators and evaluation methods used so far in literature. Furthermore, the evaluation method that is presented for evaluating the robustness of the road network against short term variations in supply (like incidents) contributes to the problem of designing robust road networks because it has a relatively short computation time and it takes spillback effects and alternative routes into account.     

Empirical evidence on cruising for parking

The literature on car cruising is dominated by theory. We examine cruising for parking using a nation-wide random sample of car trips. We exclude employer-provided and residential parking. We focus on the Netherlands, where levels of on-street and off-street parking prices are locally the same. We demonstrate then that due to this price setting the average cruising time in the Netherlands is only 36 s per car trip. Furthermore, we show that cruising is not random. It is more common in (large) cities that receive more car trips, particularly for shopping and leisure activities. Cruising time increases with travel duration as well as with parking duration. Cruising has a distinctive pattern over the day with a peak in the morning, so the order of arrival is essential to parking. Because cruising has a spatial and time component, policies may be considered that reduce cruising time through flexible pricing of parking or improved information about vacant parking spaces.     

Modelling passenger demand for parkway rail stations

Interest in Parkway stations emerged in the 1980s. These act as convenient out-of-town stations for inter-urban rail journeys. There were 13 so-called Parkway stations in Great Britain in 1999 and two have subsequently been opened. This paper reports the development and application of a new Parkway forecasting model which was conducted for the Association of Train Operating Companies (ATOC), undertaken as part of an extensive update to the Passenger Demand Forecasting Handbook, which recommends demand forecasting frameworks and associated parameters that are widely used in the railway industry in Great Britain. The objective was to develop a model that had more desirable properties and was more straightforward to apply than the previously recommended procedure. The focus is entirely upon inter-urban journeys of over 80 km.The model forecasts the demand for Parkway stations based solely on rail ticket sales data and its properties are illustrated with two case study applications. The nature of Parkway stations forces consideration of competition, and it is demonstrated that the inclusion of a station choice component leads to a somewhat improved explanatory power and a more plausible generalised cost elasticity.In addition to the methodological developments, the model has provided generally reasonable elasticities and forecasts and shown that Parkway users have different preferences to rail travellers in general. In a test based around a newly opened Parkway station, its forecasts are more accurate than the procedure it replaces.     

Abatement cost functions for incentive-based policies in the automobile market

This paper compares the outcomes of policies that target vehicle holdings with those that target vehicle usage using data from the US Consumer Expenditure Survey. Results show that a higher price of gasoline shifts vehicle holdings towards more fuel efficient vehicles and reduces the annual demand for miles, whereas imposing a fee on vehicles or a feebate program only shifts vehicle holdings towards more fuel efficient vehicles and has little to no impact on the demand for miles. While it is relatively expensive to reduce CO₂ emission through incentive-based policies, achieving any abatement level is more expensive through imposing fees on vehicles than gasoline taxes. In addition, the maximum amount of abatement attainable by a feebate program is relatively small and the same amount could be achieved by imposing a $0.73 gasoline tax per gallon.     

Strategies for Serving Peak Elevator Traffic

—Suppose that in an office building of moderate height (5 to 20 floors) elevator traffic during peak periods is predominantly between a lobby floor and various upper floors. We compare the waiting plus riding time and the average number of passengers carried per trip for various strategies. In particular, if two (or more) elevators serve the same group of floors (a common strategy), the waiting plus riding time would typically be (slightly) less if one split the group into two parts and had each elevator serve separate parts of this group. The latter strategy would have considerably fewer passengers per trip. It may also be advantageous to have a single elevator serve different floors on successive trips. Seldom would it be advantageous for an elevator to serve more than 2 or 3 floors on any trip. If elevator systems used more efficient strategies during peak periods, buildings would not need as many elevators.     

Probabilistic multi-aircraft conflict detection approach for trajectory-based operation

Conflict detection (CD) is one of the key functions used to ensure air transport safety and efficiency. In trajectory-based operation (TBO), aircraft are provided with more flexibility in en route trajectory planning and more responsibility for self-separation. The high flexibility in trajectory planning enables random changes in pilot intent, thus increasing the uncertainty in trajectory prediction and CD. This study proposes a novel probabilistic CD approach for TBO in which the uncertainty of pilot intent is taken into account by quantifying the aircraft reachable domain constrained by the flight plan. First, a probabilistic model for aircraft trajectory prediction is developed using the truncated Brownian bridge method. Based on this model, a novel conflict probability estimation method is developed. Finally, the performance of the proposed probabilistic CD approach is demonstrated through an illustrative air traffic scenario.     

Designing a decision support model for the LNG market

Abstract

As the Liquefied Natural Gas (LNG) market is supply-driven and subject to long-term contracts, both liquefaction companies and shipowners need to make strategic decisions on fleet chartering requirements. These planning decisions become ever more difficult in light of the transformations permeating the LNG market, propelling into a more competitive market with more flexible trades and expanding spot markets. The overcapacity of LNG ships during 2008–2009 triggered by massive overcontracting is a good case in point where the use of decision support models would have been beneficial, especially considering the fortunes and risks at stake. In this paper we present an LNG shipping model that effectively supports decision-making in practice. To demonstrate the value added of the model, we study the implications of LNG project delays and increased decommissioning of ships with respect to market balance and fleet requirements.     

A European vision for more environmentally friendly buses

This paper deals with EBSF – European Bus System of the Future, 3iBS – the Intelligent, Innovative Integrated Bus Systems and ZeEUS – Zero Emission bUs Systems, three research projects funded by the European Commission, with the aim to develop a new generation of buses. The common task is to develop innovative solutions to increase the attractiveness of this mode and to operate more environmentally-friendly vehicles.Key working areas are more comfortable layouts, advanced ITS-based solutions to improve operations, new engines designed to save fuel and the enhancement of the electric option. Concern for the environment lies behind the majority of these innovations. The innovations are tested in real urban environments and performance assessed through Key Performance Indicators. Within EBSF it was also possible to perform a Transferability Exercise (TE) to assess the theoretical exportability of the innovations to more urban contexts.The research objective of this paper is to critically revise the projects’ results and present them for further applications beyond the European projects field. Results thus far stressed contrasting aspects within a common vision for the development of a new generation of buses. Stakeholders are well aware of the need to comply with the European standards in the field of sustainable mobility. This is shown by the fact that the majority of them are becoming more environmentally aware about the need to renew their fleets. However, because of economical reasons they fail to consider any environmental concerns in the TE, even when these should be crucial in the transfer decisions.     

A conflict-based path-generation heuristic for evacuation planning

Evacuation planning and scheduling is a critical aspect of disaster management and national security applications. This paper proposes a conflict-based path-generation approach for evacuation planning. Its key idea is to decompose the evacuation planning problem into a master and a subproblem. The subproblem generates new evacuation paths for each evacuated area, while the master problem optimizes the flow of evacuees and produce an evacuation plan. Each new path is generated to remedy conflicts in the evacuation flows and adds new columns and a new row in the master problem. The algorithm is applied to a set of large-scale evacuation scenarios ranging from the Hawkesbury-Nepean flood plain (West Sydney, Australia) which require evacuating in the order of 70,000 persons, to the New Orleans metropolitan area and its 1,000,000 residents. Experiments illustrate the scalability of the approach which is able to produce evacuation for scenarios with more than 1200 nodes, while a direct Mixed Integer Programming formulation becomes intractable for instances with more than 5 nodes. With this approach, realistic evacuations scenarios can be solved near-optimally in reasonable time, supporting both evacuation planning in strategic, tactical, and operational environments.     

Defining and measuring service availability for complex transportation networks

Service Availability of a transportation system is a measure of a performance that has been generally defined according to the reliability and maintainability terms of mean-time-before-failure and mean-time-to-restore, as borrowed from the aerospace/defense industry. While such definitions correctly describe the availability of a system and its equipment to function they do not directly measure the percent of designed and scheduled service available for passenger use. For the more complex transportation systems having multiple tracks and routes, fleets of vehicles, more than two stations and more than one mode of service there are needs for definitions that account for isolated failures that partially interrupt or delay service. Successful definitions of service availability have been based on data that is easily and directly entered in the operating log or automatically collected by Automatic Train Supervision (ATS) system and reports generated by software. The following paper first defines measures of service availability in current use and analyzes exact and approximation methods for data collection and computation. Second, the paper postulates and explores classical and new definitions of service availability applicable for complex networks such as Personal Rapid Transit (PRT). Insight is provided for choosing a suitable definition based on the type of transportation network.     

Continuous approximation models for mixed load school bus routing

School bus routing is a complex and expensive transportation problem for many public school districts. Typical school bus routes serve a single school, but mixed load school bus routes carry students for more than one school at the same time. A mixed load policy reduces the number of stops and distance to pick up and drop off children, but it can increase travel distance by visiting multiple schools. This paper provides a general strategic analysis using continuous approximation models to assess the conditions under which mixed loading is likely to be beneficial. We also present a case study for a semi-rural Missouri school district to illustrate the application of the models in practice. Results show that mixed load routing is more beneficial for larger districts, when a large percentage of bus stops are shared by students of different schools, and when schools are closer together.     

Index numbers for monitoring transit service quality

The measurement of transit service quality is very important for guaranteeing a transport supply characterized by satisfactory service levels for the passengers. Even more important is the monitoring of the levels of service quality over time, which can be very useful to determine if the goals established by the transport planners are being met or exceeded. The status and evolution of transit service quality can be monitored through periodic and regular updating of the opinions expressed by the passengers about the service during the well-known Customer Satisfaction Surveys, allowing the effect of policies to be evaluated and specific interventions to be introduced. In this work, just the issue of monitoring service quality based on users’ opinions is approached, and the index numbers usually applied in the economic and industrial field are proposed for this purpose. Index numbers permit to study the fluctuations or variations of a variable or more variables over time, providing a powerful measurement for making comparisons and predictions of the analyzed concept. The index numbers were calculated on the basis of data collected from Customer Satisfaction Surveys addressed to the passengers of the metropolitan public service of Granada (Spain). The analyzed time period has been established from 2007 to 2013. Interesting results derive from the calculation of the index numbers. Since both perceptions and importance rates are considered in this methodology, the results can inform, not only on the satisfaction tendencies but also on the trend on customers’ priorities, which is actually the expected quality. Therefore, policies could more efficiently be designed to adjust the service to the users’ real needs.     

Predictive usage mining for life cycle assessment

The usage modeling in life cycle assessment (LCA) is rarely discussed despite the magnitude of environmental impact from the usage stage. In this paper, the usage modeling technique, predictive usage mining for life cycle assessment (PUMLCA) algorithm, is proposed as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, and formulate predictive LCA equations for current and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations.