Travel time reliability is a fundamental factor in travel behavior. It represents the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time reliability depends on the penalties incurred by the users. In road networks, travelers consider the existence of a trip travel time uncertainty in different choice situations (departure time, route, mode, and others). In this paper, a systematic review of the current state of research in travel time reliability, and more explicitly in the value of travel time reliability is presented. Moreover, a meta-analysis is performed in order to determine the reasons behind the discrepancy among the reliability estimates.

Value of Travel Time Reliability: A Review of Current Evidence

The recent trend of rapid urbanization makes it imperative to understand urban characteristics such as infrastructure, population distribution, jobs, and services that play a key role in urban livability and sustainability. A healthy debate exists on what constitutes optimal structure regarding livability in cities, interpolating, for instance, between mono- and poly-centric organization. Here anonymous and aggregated flows generated from three hundred million users, opted-in to Location History, are used to extract global Intra-urban trips. We develop a metric that allows us to classify cities and to establish a connection between mobility organization and key urban indicators. We demonstrate that cities with strong hierarchical mobility structure display an extensive use of public transport, higher levels of walkability, lower pollutant emissions per capita and better health indicators. Our framework outperforms previous metrics, is highly scalable and can be deployed with little cost, even in areas without resources for traditional data collection. The growing availability of human mobility data can help assess the structure and dynamics of urban environments and their relation to the performance of cities. Here the authors introduce a metric of hierarchy in urban travel and find correlations between levels of hierarchy and other urban indicators.

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Hierarchical organization of urban mobility and its connection with city livability

Dynamic ridesharing brings together individuals with similar itineraries to travel together and share the costs Centralized (binary integer programming) and decentralized (dynamic auction-based multi-agent) optimization algorithms are formulated to match passengers and drivers An application of these methods on a large scale case study shows that the decentralized approach provides near optimal solutions for single-driver, single-passenger case, with much less computational burden The decentralized approach is then extended to accommodate both multi passenger matches The results indicate higher user cost savings and Vehicle Kilometers Traveled (VKT) savings when allowing multi passenger rides

Agent Based Model for Dynamic Ridesharing

The disutility of transfers in multimodal public transport goes beyond the additional walking and waiting times. Although the magnitude of this pure transfer penalty has been proven to be an essential element in the structural design of public transport lines, the scarce available research reveals a wide range of values. The aim of this paper is to develop and apply a framework to estimate the value perceived and assigned by commuters to this penalty. This framework includes all the other elements considered by users in the case of a trip involving (potential) transfers, in order to obtain the impact of each one. The framework is based on the discrete choices paradigm and applied to data collected in Madrid, Spain. The results show that the pure transfer penalty is comparable to a 15.2–17.7 equivalent increase in in-vehicle minutes; i.e. longer trips may be preferred to faster alternatives with transfers, even if the additional walking and waiting times are zero. As well as the pure transfer penalty, the model also captures the effects of habit, crowding, walking, waiting and in-vehicle times, information, and the additional effect of intermodality on transfers.

Transfer penalties in multimodal public transport networks

The paper presents a general analytic framework to model transit systems that provide door-to-door service. The model includes as special cases non-shared taxi and demand responsive transportation ( DRT ). In the latter we include both, paratransit services such as dial-a-ride ( DAR ), and the form of ridesharing ( shared taxi ) currently being used by crowd-sourced taxi companies like Lyft and Uber. The framework yields somewhat optimistic results because, among other things, it is deterministic and does not track vehicles across space. By virtue of its simplicity, however, the framework yields approximate closed form formulas for many cases of interest.

A general model of demand-responsive transportation services: From taxi to ridesharing to dial-a-ride

On-demand systems in which passengers with similar routes can share a vehicle are expected to become a relevant part of future mobility, thanks to their flexibility and their potential impact on reducing congestion. Nevertheless, due to the long detours required by a door-to-door scheme, they induce extra costs to the users in terms of delay. In this paper, we face the design of such a system in which users might be requested online to walk towards/from nearby pick-up/drop-off points if this improves overall efficiency. We show theoretically that the general problem becomes more complex (as it contains two sub-problems that extend set-cover), analyze the trade-offs that emerge, and provide a general formulation and specific heuristics that are able to solve it over large instances. We test this formulation over a real dataset of Manhattan taxi trips (9970 requests during one hour), finding that (a) average walks of about one minute can reduce the number of rejections in more than 80% and Vehicles-Hour-Traveled in more than 10%, (b) users who depart or arrive at the most demanded areas are more likely to be required to walk, and (c) the performance improvement of the service is larger when the system receives more trip requests.

On-demand ridesharing with optimized pick-up and drop-off walking locations

Using a quite general parametric description of an urban setting in terms of its network, centers structure and demand pattern, we find the optimal spatial arrangement of transit lines out of four basic strategic competing options: direct, exclusive, hub-and-spoke and feeder-trunk. We identify clearly the relation between the characteristics of both the urban setting (mostly monocentric, polycentric or dispersed) and the users (transfer penalty, patronage) with the line structure that shows the best response.

Optimal public transport networks for a general urban structure

The fleet for surface transit in urban areas - number of buses and vehicle size - is usually determined by the characteristics of demand during the peak period, adjusting frequencies for other periods where those characteristics are quite different. Here the problem is formulated analytically considering a representative single line where hourly passengers flow and average trip lengths differ between peak and off-peak periods. Analytical and numerical results show that minimizing social costs (operators and users) for the whole day yields a larger fleet of smaller buses than if only the peak period is considered. Contrary to previous results on this issue, optimal frequencies across periods differ by a large amount, as optimal peak and off-peak frequencies are larger and smaller respectively than what would be obtained modeling each period by itself. The optimal bus size lies in-between the capacities obtained when each period is independently optimized. Several other interesting counterintuitive results are shown.

Optimal fleet size, frequencies and vehicle capacities considering peak and off-peak periods in public transport

Urban transport systems analysis requires some explicit or implicit representation of the network, activity pattern and flows pattern of the city. When dealing with transit design in real systems, detailed descriptions of cities are too complex to allow an analytical formulation that leads to exact results, so heuristics have been used. Alternatively, optimal design of transit systems at a strategic level has been done based on simplified descriptions using regular patterns or small networks to face and solve ad-hoc transit design problems. In this paper we propose a parametric description of cities for the normative analysis of transit systems. This is achieved after a synthesis of different ways to describe a city’s urban form that can be found in the literature, with an emphasis on the road network and the role of centers and subcenters. These diverse descriptions are assessed with the help of topological indicators and synthetic information regarding real cities. The parameters characterize the underlying network, the zones involved and the spatial pattern of transport demand, such that the design of public transport systems can be studied normatively for different city shapes. The model is applied to describe three very different real cities.

A Parametric Description of Cities for the Normative Analysis of Transport Systems

Ride-hailing (ridesourcing) companies such as Uber, Lyft, and Didi Chuxing have been a disruptive force in the urban mobility landscape around the world during the past decade In this paper, we analyse the working conditions, earnings, and job satisfaction of ride-hailing drivers We begin by discussing the regulatory, labour, financial, and urban mobility effects of ride-hailing companies Then, we present the results of a self-administered survey to ride-hailing drivers in Chile, which is complemented with the use of online tools for the estimation of driving earnings Our findings show that the flexibility to choose work times is the most appreciated attribute of this job, even though most drivers follow a somewhat fixed routine each week By contrast, the level of transparency with which ride-hailing apps determine driver pay is the attribute with the lowest satisfaction score A large number of respondents drive for long daily and weekly periods, which is a health and safety hazard Current drivers are not concerned about the future deployment of driverless vehicles for on-demand mobility services Ordered probit models for job satisfaction show that ride-hailing was better evaluated by drivers who use it as a complement to another part-time job, by those who earn more money per week, and by those who have not experienced undesirable situations while working, such as harassment or traffic crashes

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Andrés Fielbaum

Papers

On-demand ridesharing with optimized pick-up and drop-off walking locations

Optimal public transport networks for a general urban structure

Optimal fleet size, frequencies and vehicle capacities considering peak and off-peak periods in public transport

A Parametric Description of Cities for the Normative Analysis of Transport Systems

The sharing economy and the job market: the case of ride-hailing drivers in Chile