AUTOlab staff and students conduct applied research, leveraging real-world data received through agreements with transportation companies and government agencies.
Design and operation
of automated parking lot systems
The rise of innovative transportation services may allow more citizens to give up car ownership. Nevertheless, as the urban population keeps increasing, there is still a great need to efficiently utilize land resources designated for parking purposes. The potential social, environmental and economic benefits of doing so are quite clear. In this research we intend to develop models and methods for the design and operation of automated parking lot systems.
Specifically, we wish to answer questions regarding the efficient routing and scheduling of robots and elevators, the value of user information in reducing waiting time, promising parking layouts, etc.
Design and operation
of semi autonomous public transportation systems
Autonomous vehicles are projected to reshape urban mobility in the near future. This technological advancement is promising to reduce energy consumption, pollution, accidents and congestion while simultaneously increasing transport accessibility and improving the travel experience. Full self-driving vehicles are expected to become commercially available in the coming decade. However, the autonomous vehicle market is expected to completely mature in three to four decades. This long period, opens the door for novel transportation services that partially utilize autonomous capabilities.
In this project, we study the design and operation of an innovative last-mile on-demand public transit system consisting of convoys of electric vehicles. Each convoy is composed of a human-driven head vehicle followed by one to several cabins that can autonomously travel short distances at the proximity of stations. That is, cabins can attach and detach from head vehicles as they pass nearby stations while heads cycle non-stop in the system. A passenger completes its entire journey on-board the same cabin, while the cabin may switch several heads along its path. These features generate strong dependencies between the head-cabin-passenger layers. Namely, passenger movements are restricted by cabin movements, which in turn are restricted by head movements. Operationally, this results with a complicated routing and assignment problem.
Novel transportation services in the era of autonomous-connected-electric vehicles
Current technological advances in vehicle autonomy, vehicle connectivity and vehicle electrification can be harnessed to reshape today’s public transit services in order to provide improved services over the same existing transit network. In this research project, we wish to examine the potential of transforming traditional fixed route transit
to flexible on-demand transportation services by utilizing these technological advances. Such transformation may allow offering shorter trip times, operating at peak-hours similar to traditional public transit and scaling down without compromising service quality.The proposed research will develop a holistic framework for transforming traditional public transit systems and for efficiently managing the resulting transit systems.
Multi-modal on-demand transportation services
In this research we develop a new variant of the Dial-a-Ride Problem (DARP) that includes possibilities for transfers between vehicles and permits walking segments. The problem generalizes the classic form of the DARP, which is known to be NP-hard, and therefore is also computationally hard. Our aim is to solve large scale problem instances that are based on real-world scenarios by devising effective heuristic methods.
using electric autonomous vehicles
Ride sharing is transforming urban mobility by offering reliable and convenient on-demand services at any time. Given the constant increase in demand, ride sharing businesses are currently planning to expand their portfolio to include Dial-a-Ride services by the use of electric Autonomous Vehicles (AVs). This novel type of service introduces new operational challenges. First, as the vehicles are electric, battery management need to be considered during route planning. Second, autonomous vehicles can serve non-stop and are not required to return to a specific depot. Providing multiple depots becomes a crucial feature since vehicles will need to continuously wait and relocate around the urban network during the non-stop service.
Several operational planning elements are considered in this project, including: 1) offline planning for a set of fully known in advance service requests 2) real-time adaptation of existing vehicles plans to incorporate new requests appearing online 3) efficient scheduling of given vehicle routes – crucial for effective planning of both the offline and online cases. Real data from Uber Technologies Inc. in San Francisco is employed for testing purposes. A solution approach for the first element (1) was published in Transportation Research Part B.
Relocation strategies in one-way carsharing systems
In this research we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic station-based relocation policies.
Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. We have conducted a first of a kind field experiment in collaboration of a carsharing operator in France. Numerical results from the experiment and an extensive simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy. This research was published in Transportation Research Part B.
On-Demand Taxi Service Behavior Upon A Road Hierarchy
We are analyzing an on-demand transportation dataset published through the GAIA Initiative that includes DiDi Express and DiDi Premier rides for November 2016 in Chengdu City, the capital of Sichuan province in China. The research is motivated by the high economic and environmental cost of traffic congestion worldwide and the possibility of shared mobility to reduce these costs. The project expands upon previous research conducted using the same dataset (especially Li et al 2019) employing both exploratory data visualization techniques and statistical methods to identify a tendency towards pick-ups on major roads and drop-offs on minor roads. The project also examines differences between shared and non-shared trips and service-use at different times of day.
In Sankey diagrams, the width of the lines is proportional to the magnitude of flow. The road categories are ordered to minimize crossings. Very few pickups or drop-offs occur on Motorway/Trunk roads. The majority of pickups on Primary roads end in drop-offs on Tertiary roads.
Brightly colored areas host more pickups than darker areas. The road network is not specifically rendered, but is clearly visible due to the diversity of pickup locations.
Brightly colored areas host more dropoffs than darker areas. The road network is not specifically rendered, but is visible due to the diversity of dropoff locations. The colors tend to be darker because the dropoffs are more dispersed than the pickups, including more minor roads.
DiDi drivers picked up almost no passengers on the motorways from 8pm to 3am. Pickups on residential roads remained common during those times.
User dissatisfaction in the presence of unusable bicycles
In this project, we studied the impact of unusable bicycles on the quality of service provided by bike-sharing systems. We modeled user dissatisfaction by a weighted sum of the expected shortages of bicycles and docks as a function of the initial inventory of usable and unusable bicycles. We analyzed and proved the convexity of the resulting bivariate function and accurately approximated it by a convex polyhedral function. Future studies can make use of the resulting polyhedral function in linear optimization models for operational and strategic decision making in bike sharing systems. In addition, our numerical results demonstrated the high effect of the presence of unusable bicycles on user dissatisfaction. This research was published in IISE Transactions.
Online data driven detection of unusable bicycles and docks in a bike-sharing station
In most station-based bike-sharing systems, there is no reliable on-line information that indicates the usability of bicycles. Such information is crucial for the efficient planning of maintenance operations. In this project, we developed a Bayesian model that estimates the probability of a specific bicycle to be unusable, based on available trip transactions data. Further on, we devised some information based enhancements of the model and presented an equivalent model for detecting dock failures. We validated our detection model using a simulation model that is based on data from a real-world system and demonstrated that it predicts well, in real-time, thae number of unusable bicycles in a station. This research was published in Omega.
The impact of parking reservations in vehicle sharing systems
In this work, we examined the impact of regulations on the performance of vehicle sharing systems. In particular, we proposed implementing parking reservation policies and demonstrated via a theoretical queuing model and an enhanced simulation model that such policies can significantly improve the performance of vehicle sharing systems. The numerical study was based on data derived from real-world systems such as Capital Bikeshare and Tel-O-Fun. This research was published in Transportation Research Part B and European Journal of Operations Research.
Integrating line planning and timetabling for passenger trains
Railway planning, consists of several fundamental phases, including line planning, timetabling, rolling stock circulation, platforming, and crew scheduling. Traditionally, the planning process is done hierarchically such that the outcome of one phase serves as an input for the following. In this work, we have developed an optimization model that integrates the line planning and timetabling planning phases, with an objective of reducing the total time users spend in the system. Thanks to Israel Railways planners, we were able to test our solution method on the Israel railway system. We
have bench-marked our approach against the line plan and timetable of Israel Railways and demonstrated that using the same amount of resources, the average journey time of passengers could be reduced by 20%. This research was published in Transportation Science.