Mission

To solve tactical challenges using interactive Operations Research and Machine Learning-driven decision-making tools.

Interactive Operations Research

Operations research (OR) is a field of mathematics that uses analytical methods for better decision-making. Despite its long life since its birth at the Second World War, the field has advanced further on “Research” than “Operations.” Interactive-OR is our R&D lab whose objective is to enhance OR applications by developing tools that are interactive, data-driven, intuitive, accessible, and responsive.

 
 

Interactive

Administer Design Changes Interactively

Data-Driven

Data-driven Predictive/Perscriptive Analytics

Intuitive

Requires No Technical Software Training

Accessible

Full-Stack Cloud-based With Login and Save/Load Capability

Responsive

Instantaneous Analytics and Results on Designs

 
 
 

Tools Vs Dashboards

Tools are commonly mistaken for dashboards despite their structural differences. Dashboards provide a visual data interface that presents a consolidated and appealing view of key information and metrics for easy comprehension. While users can interact with data filters in a dashboard, they cannot manipulate the data itself since the dashboard, by nature, is a data visualizer and not a data manipulator. In contrast, a tool is a data visualizer and a data manipulator, allowing mobility modelers and full-stack developers to embed optimization and prediction models on the front/back end to perform user-defined processes.

 

Use Case of Mobility Growth Plans

A mobility growth (master) plan is a comprehensive and long-term plan developed by transportation authorities or consultancies within the relevant industry to address and manage the growth of transportation systems within a region. The outcome of the plan is a detailed report that summarizes the main findings, recommendations, and strategies for the region's transportation development. These reports are invaluable resources for guiding transportation investments and policy decisions over the long term. A drawback is that they are typically static documents that are updated infrequently (e.g., every 4-5 years), disallowing planners to adapt to changing circumstances or explore alternative scenarios.

Addressing this limitation, the proposed tools from interactive-OR are designed to complement and, in some cases, replace traditional growth plans. The tools permit planners to conduct a wide range of "what-if" scenarios in the design process and respond to evolving transportation challenges and opportunities in a more agile manner than traditional mobility growth plans alone. In addition to growth plans, the tools can be used in operational day-to-day decision-making, such as micro-mobility bike-charging logistics or drone delivery routing.

 
 

Applications

Micro-mobility

  • Fleet and Station Logistics Planning

  • Electric Fleet Management

  • Equity and Multi-Modal Integration Analysis

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Traffic Management

  • Data-Driven Signal Optimization

  • Mode and Trajectory Detection Using Vision AI

  • Automated Speed Enforcement Logistics

Electrification

  • Electric Vehicle Charger Station Planning

  • Electric Vehicle Adoption Prediction

  • Charger Availability Analysis

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Transit Planning

  • Fleet and Charger Logistics Planning

  • Optimal Bus Charging Scheduling

  • Performance Analysis of Status Quo Networks

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Parking

  • Adaptive Parking Rate Optimization

  • Parking Occupancy Prediction

  • Illegal parking and Revenue Analysis Using Parking Transaction Data

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Last-Mile Delivery

  • Drone Routing Optimization

  • Synchronized Drone/Truck Trajectory Planning

  • Subscription-based Delivery Programs

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Transit Network Design

Transit route planning is a multifaceted process involving strategic decisions such as selecting bus stop locations, determining design headways, generating bus trips and ultimately bus blocks. The practice of transit network planning often remains cumbersome and manual, largely due to the gap between theoretical frameworks and practical applications. Existing transit planning software, while intuitive and attractive, frequently overlook the rich insights provided by transportation theory, such as the four-step model, resulting in a disconnect between research and application.

TransNet-Pro is a transit network planning and analysis tool that integrates the comprehensive four-step model of trip generation, trip distribution, mode choice, and route assignment into the transit planning process on a web-based platform. This integration not only streamlines planning efforts but also ensures that decisions are grounded in empirical evidence and theoretical insights, thereby enhancing the efficiency and effectiveness of public transportation systems.

The video first shows the balance of origins and destination. The stop locations are selected by clicking and drag-dropping, and the fleet properties (number and capacity of buses) are adjusted at the left panel. The thickness of the route (red line) represents the crowdedness of that segment, which changes with fleet size and bus capacity. The red circles represent the number of waiting passengers, and the blue ones are the moving buses.  The results are presented at the bottom left corner and animated on the map as well.

 
 

Transit Fleet Electrification

Transit fleet electrification is a three-sided problem as it requires 1- Finding the best charger locations, 2- Optimizing the charging schedule, and 3- Selecting the ideal electric bus type. GreenTrans-Pro is a platform that maximizes the number of electrified buses while optimizing their en-route charging schedule subject to operational constraints, such as the limited number of outlets at each station, battery capacity, minimum safety charge, and allowable layover times for charging. The tool summarizes fleet performance, charging profiles at each station, and the charging schedule for each bus.

 
 

Bikeshare Planning

Bike-share growth (or master) plans involve detailed research and analysis to determine optimal locations for new bike-share stations, dock capacity, and fleet size and mix. Although growth plans provide a valuable expansion guideline, their low frequency (once every 4-5 years) means bike-share managers cannot easily deviate from the recommendations unless follow-up studies are requested. The features of the tool include:
 
1-    Add/modify station properties such as dock capacity and charger availability,
2-    Predict rider flow between stations, including newly added ones,
3-    Investigate and visualize transit integration efficiency,
4-    Assess micro-level properties of stations, including proximity to points of interest,
5-    Optimize new bikeshare station locations based on equity weights,
6-    Assess the fleet size and mix impacts and investigate e-bike expansion programs.

 
 

Electric Vehicle Charger Planning

Electric vehicle charger planning for intra-city travel is complex as it requires multiple data sources on existing charger locations, work and leisure-related trip patterns, points of interest (POI) for deploying new stations, EV ownership populations, power network properties, and sociodemographic features. EV Charger Pro is an interactive tool that creates comprehensive expansion plans with versatile analytics, including charger location optimization, POI assessment, and "Network Science" analytics.
 
Below is a demonstration of three features of the tool. The ward shades are first chosen to represent the ratio of DC outlets over the population of EV owners in each ward. A DC station is then added and relocated between wards, while the POIs are updated based on the new locations. Lastly, the optimization engine is executed to provide guidance on the ideal wards for the new charger.

On-street Parking Pricing

A highly neglected source of on-street parking occupancy data is parking payment transactions. In contrast to costly, hardly scalable, and infrastructure-invasive sensors, parking payment data is widely accessible with a wealth of information on parking behavior. On the downside, this data does not capture permit-holders, illegal parkers – those who don’t pay at all and those who overstay their payment, and early departers- those who leave earlier than granted.

ParkPlan-Pro is a web-based tool that uses ID-anonymized transaction data from Green-P and developed a parking occupancy prediction model using a Graph Neural Network and other ML benchmarks. We embedded the prediction model into an interactive parking pricing tool that allows parking analysts to test various pricing scenarios. We have so far developed hourly, time-of-day, and progressive pricing models. The tool also includes geojson boundaries of the Green-P parking zones, which were digitally surveyed and geocoded.

Retail Analytics

Retail-Pro is a tool that allows for answering questions such as the following:

How accessible are major grocery retailers to market segments of different income, age, and other socio-demographic features?
How does accessibility to grocery retailers change with the mode of transportation (walking, cycling, driving)?
Which retailer is best for distributing a product targeted at a specific market segment? For example, single parents within a 10-minute walk of a store.
What is the level of competition, defined in terms of spatial overlap in coverage, of major grocery retailers, and how does it change with accessibility?
What retailer provides more equitable access?
Where should retailers locate new stores?

 
 

Drone Delivery Routing

Drone delivery offers the promise of quick and efficient package transportation, potentially reducing road traffic and carbon emissions. We developed an algorithm and an interactive tool to minimize the total delivery time by coordinating the trajectory of a truck and multiple drones.

The tool allows changing the drone features s such as range, speed, and fleet size. Follow the instructions on the left. Subsequent versions of the tool will include multiple trucks, multi-drone launching, hovering restrictions, no-flight zones, 3D drone navigation, and onboard charging. 

Vehicle Routing Problem

The Vehicle Routing Problem is to find the most efficient routes for a fleet of vehicles to deliver goods or services to a set of destinations, typically with constraints like vehicle capacity.

The problem was first posed by George Dantzig and John Ramser in 1959 in their paper "The Truck Dispatching Problem," published in "Management Science." Since then and according to Google Scholar, more than a million papers have been dedicated to solving this problem given its complex combinatorial nature.

Despite the large body of literature, practical applications of the problem on real networks are rare. The following tool demonstrates our front-end algorithm for the vehicle routing problem. You can click on the map to add points, move them by drag/dropping, and change the fleet size and capacity of the vehicles.
Reach out to us if interested in testing it out.

Two-Echelon Vehicle Routing

The Two-Echelon Vehicle Routing Problem is a logistics optimization challenge of transporting goods from a central depot to customers through intermediate satellites in two stages: first using larger vehicles to satellites and then smaller vehicles for final recipients. The latter is also known as the last mile.

The two modes of transportation can be picked from any of driving, cycling, walking, or even transit depending on the application. An example would be when a delivery driver finds a parking space to make a collection of deliveries within a certain proximity on foot, choosing driving as the first and walking as the second mode.

We developed a two-echelon vehicle routing algorithm and optimized it for speed and efficiency in terms of minimizing total delivery time within a reasonable computation time using real routing data. The tool allows for picking the two modes of transportation, and the capacity and size of the second mode’s fleet.

Generative VRP

Last-mile logistics planning requires measuring potential delivery costs to assess whether a particular market is profitable. VRP-Gen is a web-based vehicle route generator that takes a service area as input, randomly generates customers uniformly or based on customer densities, solves variants of VRP, and tracks system performance metrics such as total delivery time.

 
 

3D Navigation

The drone/UAV allowable flight altitude can vary depending on the country and the specific regulations in place. In many countries, recreational and commercial drones are limited to an altitude of 400 feet (about 120 meters) above ground level to avoid interference with manned aircraft. This is a common regulation set by aviation authorities like the Federal Aviation Administration (FAA) in the United States.
 
DroneNav-Pro is 3D drone/UAV navigation tool that minimizes the total energy consumption of travel from an origin to a destination (the two circles in the video). Energy is consumed differently for horizontal flight (i.e., cruising), descent, and climb movements. The tool takes as input a desired flight altitude and finds an energy-minimizing path that prioritizes that altitude while avoiding buildings by their height; the hexagon shades represent building heights. The algorithm uses a vertically integrated honeycomb network to find the short path in terms of energy consumption. The height profile of the flight is at the bottom.

Electric Vehicle Navigation and Charging

An electric vehicle navigation and charging optimization tool that can be customized for vehicle properties and driver preferences. The tool allows modifying the battery capacity, start and finish state-of-charge, charger type, and origin-destination pairs within any city in North America. The outputs are the state-of-charge profile, total travel distance and time, and the charging information at selected stations.

Multi-Modal Navigation

Multi-modal navigation platforms facilitate seamless transitions between different modes of transportation. The caveat is the lack of customization, as the route recommendations are limited to pre-programmed mode sequences, leaving little room for personalized preferences. The simple navigation tool in the video allows picking two transportation modes in the City of Toronto and the preferred distance ratio of each. The transfer points include Park&Ride stations, parking lots and bike-share stations. The recommended routes are ranked based on sustainability metrics.

 
 

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