ProfessorDavid Levinson

Project Opportunities

 

Title: Behavioural Route Choice

 

Summary of opportunity:

 

Route Choice models have long been based on the assumption that travelers choose the shortest travel time path between their origin and destination, despite increasing evidence that is not the case. Robust models based on empirical data of how travellers do select routes remains an open question. This research will use empirical data on actual route choices from GPS and econometric methods to better predict actual routes selected. 

 

Opportunity synopsis:

 

This research will analyse existing GPS data sets of traveler routes between homes, workplaces, and other destinations and use statistical (econometric) and machine learning methods to develop empirical models of route choice. The models will be applied to large networks and compared with traditional equilibrium models and observed data. Datasets on highway and bicycle routes are available.

 

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Project Opportunities

 

Title: A Benefit/Cost Analysis of Benefit/Cost Analysis

 

Summary of opportunity:

 

Transportat projects of significant size and scope often must be subjected to fairly rigorous project evaluation processes.  Benefit-cost analysis (BCA) is a project evaluation method employed by many organizations for screening projects that represent major investments of resources.   In theory, the information acquired through BCA or similar procedures yields value by reducing the uncertainty surrounding the choice between competing alternatives.  Given that the evaluation process itself requires extensive data collection and analysis, it consumes valuable resources that, in and of themselves, have opportunity costs.  When should this type of evaluation be employed?  What type of value does it provide?  Is it worthwhile for projects that represent smaller investments?  How detailed should the evaluation be?  These are the types of questions that this research proposes to provide more concrete answers to.

 

Opportunity synopsis:

 

In terms of evaluating the use of Benefit/Cost Analysis (BCA), this research will draw on existing projects for which an ex ante BCA was completed.  It will identify projects where multiple alternatives were defined and analyzed, where “alternative” can be used to indicate a build vs. no-build option or different alignment options for the same project.  Using operational data following implementation, the research will identify 1) How correct the analysis was.  That is, how closely did actual results conform to those identified in the ex ante evaluation, and were the actual benefits higher or lower than anticipated.  Also, the research will identify 2) If the best alternative was chosen how large were the net benefits relative to the second-best alternative identified.  This value will provide an estimate of the benefits to be gained by conducting the analysis.  A related question that may be important to ask in each case is whether the result of the ex ante BCA affected the outcome of the choice between alternatives and, if it did not, what other criteria were deemed important in the final decision.

 

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Project Opportunities

 

Title: Characterizing Transport Networks

 

Summary of opportunity:

 

In an increasingly urbanized world, people remain connected by a complex nexus of roads, rails, paths, and sidewalks that form urban transportation systems and shape travel demand. On the supply side, transportation systems possess measurable topological and spatial network properties. On the demand side, travel demand also possesses properties, and recent research observes a Macroscopic Fundamental Diagram, which illustrates a certain pattern of capacity utilization. The central hypotheses of this proposal are: (1) a direct causal relationship exists between transport network structure, the accessibility the network provides, and the quantity and nature of travel demand (e.g. trip frequency, trip distance and time,  activity space, and mode share. ) served by that network (after controlling for the network size); (2) this relationship is largely influenced by network structure, such as more complex networks result in less travel per capita; (3) this relationship is scalable (i.e. similar patterns occur in small and large cities), akin to many other urban properties.

 

Opportunity synopsis:

 

This research will characterize cities according to their transportation systems, both from the supply and demand sides, which will provide a means to determine the presence of natural phenomena (e.g., between-cities comparisons, scaling) It will estimate the relationship between metrics of transportation network structure, particularly connectivity accessibility, and travel demand, using machine learning algorithms and testing a variety of hypotheses (e.g. does network complexity reduce travel demand?)

 

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Project Opportunities

 

Title: Simulating Transport for Realistic Engineering Education and Training

 

Summary of opportunity:

 

Understanding and predicting travel demand and travel patterns is one of the most important but also most challenging tasks of transport engineers, planners, and geographers. Travel forecasting models are complex and nearly impossible to use in the classroom. The Internet provides a more attractive frontier for computer simulation in transport education. The University of Minnesota has previously developed a set of web-based simulation modules for use in the classroom to improve instruction for the Introduction to Transport Engineering course. These open source, Java-based, platform-independent learning modules have been deployed for testing and are in use in over a dozen transport classes across the United States and Australia and are publicly available at (http://street.umn.edu). This research will systematize, update, extend, and test in the classroom this new educational platform.

 

Opportunity synopsis:

 

In STREET, one module, ADAM (Agent-based Demand and Assignment Model) simulates travel patterns on a modally-independent street network based on microscopic decision- making by each traveler. It uses a stylized network, which is therefore smaller (but computationally faster) than realistic urban networks. The platform should be extended in several directions. One directions is to:•    Expand the demand model with public transit lines (a series of nodes and links with some parameters for headway, etc.) using a hypernetworks approach, following the standard GTFS specification for transit network coding; •    Implement a realistic set of preferences for travelers regarding weights associated with alternative transport technologies, so that mode share emerges from the route choices of individual travelers; •    Incorporate land use parameters more explicitly for trip generation and attraction; •    Compute network measures as described. By adding/deleting nodes or links, the network structure implicitly changes, so do network measures and observed travel behavior.

 

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Project Opportunities

 

Title: Historical and Prospective Adoption Rates of Transport Technologies

 

Summary of opportunity:

 

Traditional transportation forecasts do not consider changes in technology. Yet we know technologies change, and those changes consequently affects underlying behaviours. Providing insight into changes can help more fully inform transportation decisions. We can gain such insight through the analysis of the adoption of a wealth of historical transport technologies, which started out as options on new high-end vehicles and eventually became standard, ranging from anti-lock braking systems to in-vehicle air conditioning. This may provide insight into the adoption rates of Automated Vehicle and Electrical Vehicle Systems.

 

Opportunity synopsis:

 

This research examines a series of historical technologies in depth.  For each of the studied technologies (for instance, from Ward’s Automotive data is available on: Automatic Transmission, 5-Speed Transmission, 6-Speed Transmission, All-Wheel Drive, 4-Cylinder Engine, 5-Cyllnder Engine, 6-Cylinder Engine, 8-Cylinder Engine, Stability Control, Antilock Brakes, Power Door Locks, Power Seats,, 4 or 6 way Memory Seats, Power Windows, Sun Roof, Side Airbags, Side Curtain Airbags, Navigation System, Keyless Remote, Air Conditioning, Automatic Temp. Control,  Air Conditioning, Manual Temp. Control, Limited-Slip Differential,  Styled Wheels,  Automatic Headlamp, Cruise Control).

 

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Project Opportunities

 

Title: Network Econometrics and the Evolution of Transport Systems

 

Summary of opportunity:

 

Network Econometrics is a new set of methodologies to exploit network information when undertaking econometric analysis. It has uses in both short and long-term traffic prediction. This research will test and extend its applicability for other domains, such as predicting network structure.  The basic idea is to systematically develop a weight matrix for use in statistical analysis to weight the effects of neighbours on an outcome variable. Unlike primitive spatial weight matrices which look at some form of adjacency, the network weight matrix considers network structure, so complementary and competitive observations (e.g. links) are treated differently (flows on competitive links affect flow on the link in question negatively, complementary links are positive). The measure of betweenness, computed in a network reliability context, is used to determine whether links are complementary or competitive.

 

Opportunity synopsis:

 

Transport networks possess distinct characteristics that change over time and space. For example, capacity changes spatially, but may or may not change temporally. Although it does not change over a short period of time, it may change over a long run. The betweenness depends upon the measurement method. It may or may not change temporally but must change spatially. Total system demand (e.g. regional population) changes temporally, but is uniform spatially. The proposed network econometrics approach is able to incorporate both spatial and temporal exogenous variables and should be able to be extended to forecast the evolution of transport system, using historic data about the structure of the network and the attributes of that network.

 

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Project Opportunities

 

Title: Technological Change and the Future of Cities

 

Summary of opportunity:

 

Many transport analysts envision a forward-looking, ambitious and disruptive cloud commuting-based transport system for future smart cities based on emerging connected, autonomous vehicles (AVs) or “self-driving” cars. Employing giant pools of self-driving cars, this transport system supplants personal vehicles, low ridership public buses, and taxis used in most of today’s private and public transport systems, integrates various modes of transport in a unified, on-demand fashion, and provides passengers with a fast, convenient, and low cost transport service.  “Mobility-as-a-Service” (MaaS) system can be thought of as a smart cloud commuting system (SCCS). In addition, SCCS have the potential benefits of “greening” the future city with less traffic (thus less congestion) and lower emissions (even in the absence of electrification, lower still with electrification) as well as freeing up valuable urban land (that has been used for parking) for housing and other better land uses. With AVs replacing conventional vehicles, they essentially become roving hardware that carry people within an urban or metro region, where all AVs driving on the road are members of this SCCS. How will this futuristic dream reshape land use? How will it reshape the allocation of roadspace between vehicles, public transport, green space, sidewalks, and other uses. How will valuable curb space be managed? How should cities prepare now for changes that will take place not overnight, but over the course of decades?

 

Opportunity synopsis:

 

Infrastructure decisions are long-term. Even today Italians travel on the Via Appia of ancient Rome. Getting these decisions right is important because they are largely irreversible. But how we use the fixed rights-of-way and infrastructure we produce is much more malleable. In principle, simple inexpensive paint and signs dictate whether a part of the road is for moving cars, trucks, buses, bikes, pedestrians, or loading or unloading goods or people, or storing vehicles. Today, in practice, the politics of taking away capacity from one use (e.g. parking) for another (e.g. bike lanes) is very difficult.  In this new emerging world of automated, electric vehicles operating in the mode of “mobility-as-a-service” a number of questions arise making the issue even more complex than today. Can on-street parking, vehicular movement, and curbside pickup/dropoff mix? Who manages the curb and how? What existing models of curb management or roadway management can be applied? Can we learn from the landside at airports, where serving passengers is the dominant use? This same environment raises questions about land as well to be addressed by this research. The use of land depends on the access that is provided by the network, places with more access tend to get more intense development. Busy streets, transit stops, and stations, attract commercial uses. How does this change? Where will cars be stored? What will happen to existing obsolete uses of land (like parking structures, petrol stations, and the like).

 

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Project Opportunities

 

Title: Travel Behaviour Over Time

 

Summary of opportunity:

 

The Bureau of Transport Statistics of Transport for NSW has conducted an annual Household Travel Survey since 1997 and historically in 1991 and 1981 as a Home Interview Survey. This current year data enables the region to better understand how residents make decisions about whether, when, where, why, and how to travel. The historic data allows us to understand how changes to transportation networks (both transit, highway, and non-motorized), the use of land, and demographics and socioeconomics have affected travel behaviour. This research investigates changes in behaviour over time and space, examining in turn the effects of investment, development, and population change on behaviours for Sydney region as a whole and for areas within the region. This will inform planners and decision makers about the prospective effects of future changes to networks, land use, and demographics.

 

Opportunity synopsis:

 

A core question in transport planning is the efficacy of new investments and services. Investments can be measured both in terms of the scale of the networks available (e.g. lane km of highway, density of streets, hours of transit service), as well as the network structure or patterns of those systems (the hierarchy of roads, the topology or connectivity of the network, the morphology or shape of the network). The pattern of transport networks may affect user's perception of investment in different ways, some investments may be perceived as shortening travel times more than others (e.g. circuitous routes or routes with many stops are perceived as longer than they actually are). Combining data from this survey of traveler's reported travel times with other sources will allow us to correlate reported and actual travel times and explore their differences, and their consequential effects on behaviour. For instance, using the longitudinal time series of the surveys, we can examine the association between proximity/access to transit services and transit use behaviour for both commute and personal trips. This research can examine the impact of transit investments on changes in travel behaviour, including increased transit trip frequency, mode shift, transit ridership, travel time savings, and trip lengths, controlling for land use and demographics.

 

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Project Opportunities

 

Title: Traffic Programming - Algorithms for Cooperative Behaviour among Unconnected AVs

 

Summary of opportunity:

 

Current AV algorithms aim to safely manoeuvre a vehicle through traffic without involving humans. These algorithms do not consider implications on the efficiency of traffic as a whole. Thus, none of the existing models and methods addresses the question of how to optimize traffic from the decentralized perspective. Traffic programming will consider the environment around the vehicle and how to react to it, designing algorithms to control vehicles in real-time, accounting for the benefit of travellers collectively, while ensuring safety for the vehicle and its occupants.

 

Opportunity synopsis:

 

Algorithms occur at several scales. The highest level, scheduling and sequencing of trips, is most appropriate for freight and empty vehicle repositioning, as humans in personal vehicles will tend to want control in such decisions. The next level, routing, determines what paths vehicles use to get between origin and destina- tion. There is a large literature in transport on System Optimal vs. User Equilibrium routing, the differences between the two, and how prices can incentivize users to behave in a system optimal way. With appropriate price signals, we can align the system optimal interests of the network as a whole with the user optimal desires of the individual (autonomous or not) vehicle. Encouraging only a few vehicles to switch from one route to another is unlikely to be successful, as some vehicles switching off a route will be replaced by others switching on to it. 
The level below that is vehicle control within a route, includes technical control problems like lane-keeping, and car-following, and more interactive games like lane-changing and merging. The issue of when and how to change lanes, when to yield right-of-way and not, whether to open up a gap, or seize the gap has implications for the efficiency of traffic flow. Every lane change has the potential to waste capacity (as gaps are being occupied in two lanes by the changing vehicle), though may be necessary for particular routes, to spread traffic, or to avoid future problems. Merging vehicles from on-ramps have also been modelled using game theory. The solution to these games to maximize safety differs from the solution to maximize private gain, and from the solution to maximize social welfare. Unlike the route choice literature, little attention has been paid to these issues at the microscopic vehicle interaction level. 
Whether pricing can provide the right incentives during the give-and-take of traffic merging and lane changing is less clear. Drivers today negotiate that through eye contact and the actions behind the wheels. We imagine unconnected AVs will engage in a series of cooperative moves and can sense in real-time if these moves are reciprocated by the vehicle that is being ‘negotiated’ with. Finding protocols to do this automatically, and to observably negotiate via action and reaction directly between cars in real-time will be the task of the traffic programmer. This task will develop and simulate such algorithms.

 

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Project Opportunities

 

Title: Environmental pollution caused by tire and brake wear

 

Summary of opportunity:

 

Research Area:

Environmental Engineering, Transport Engineering, Mechanics, Urban and Infrastructure Planning, Soil Physics and Biogeochemistry

 

Opportunity synopsis:

 

The project covers the environmental pollution caused by automobile tire and brake wear and emphasizes the generation and environmental fate of micro and nano particulate originated from wearing across typical tire and brake life cycles. Tire- and brake-generated  particulates are a very underrepresented source of pollution but at the same time the problem is highly diffused worldwide with radically unknown consequences. Tire and break wear contamination in the environment is not yet fully unraveled but it is potentially as pervasive and detrimental to the environment and biodiversity as the one related to microplastics. The project may cover various cross-disciplinary topics, including mechanics, environmental engineering, hydrological processes, and land and soil processes. The project can have wide applications including in environmental quality assessment, urban and infrastructure planning, soil physics and biogeochemistry and others.

 

----------Current Research Projects----------

 

• Traffic Programming: Design of micro-decisions in automated transport.
• Reducing pedestrian crashes through better intersection design
• Strategic scheduling and deployment of random breath and drug testing operations