{ "title": "Bikeshare Pro 🏆", "yearrole": "2023 · Lead R&D", "sub": "Ridership prediction, Station planning, Electrification, Transit integration", "img": "/images/bikeshare.png", "desc": "A web decision-support tool for bike-share expansion, charger deployment, and multimodal integration, built on optimization and predictive models. Award-winning, piloted with partners, and scaled to 15+ cities.", "links": [ {"label": "Tool Description", "href": "https://interactive-or.net/bikesharepro"}, {"label": "Video", "href": "https://www.youtube.com/watch?v=JYsBq0uCxRQ&list=PLMQSwmI9u1hxwkDlHrtIriCNH1xZ1m863"}, {"label": "Paper 1", "href": "https://www.sciencedirect.com/science/article/pii/S0965856425003349?via%3Dihub"}, {"label": "Paper 2 Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_flow-maximizing-facility-location-using-continuum-activity-7291560428415991808-4foT"} ] }

{ "title": "Real-Time Bike-Share Relocation Optimization", "yearrole": "2024 · Researcher & Product Developer", "sub": "Optimization for full and partial bike relocation", "img": "/images/relocation.png", "desc": "Using a real-time API, we ingest station status and run a heuristic with a TSP solver to generate relocation routes that respect operational constraints (capacity, time windows), enabling partial or full rebalancing.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_bikesharetoronto-activity-7293717346311946240-xz3H"} ] }

{ "title": "E-Scooter Planner", "yearrole": "2024 · Lead R&D", "sub": "Pick-up and drop-off prediction with transit integration", "img": "/images/scooter.png", "desc": "Planning and analytics for free-floating fleets with demand prediction that incorporates weather, supports short- and long-term horizons, and integrates nearby transit stops and ridership.", "links": [ {"label": "Demo", "href": "https://lnkd.in/p/gu4aBFRj"}, {"label": "Collaboration Highlight", "href": "https://lnkd.in/p/g-WvWG3x"} ] }

{ "title": "Multimodal Trip Planner", "yearrole": "2022 · Lead R&D", "sub": "Routing with KPIs: GHG, calories, transfers", "img": "/images/multimodal.png", "desc": "Integrates bikeshare, transit, and driving in a single trip planner, with end to end routing and KPI comparisons (time, cost, GHG, transfers) to surface the best option for each scenario. This was my first project!", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/feed/update/urn:li:activity:7082171999480926208"} ] }

{ "title": "Peer-to-Peer Ridesharing", "yearrole": "2024 · Lead R&D", "sub": "Supply and demand analysis using LLMs", "img": "/images/ridealike.png", "desc": "Descriptive analysis for marketplace supply and demand using LLM workflows.", "links": [ {"label": "Demo", "href": "https://lnkd.in/p/g86FbMJY"} ] }

{ "title": "EV Charger Pro", "yearrole": "2024 · Product Developer", "sub": "EV demand forecasting and charger siting", "img": "/images/evpro.png", "desc": "EV charger siting with scenario planning that integrates travel survey patterns and vehicle ownership data to target high-use locations.", "links": [ {"label": "Demo", "href": "https://interactive-or.net/evchargerplanning"} ] }

{ "title": "Electric Transit Scheduling Pro", "yearrole": "2024 · Researcher & Lead Product Developer", "sub": "GTFS-based in-route charging optimization", "img": "/images/etransit.png", "desc": "Scheduling support for electric bus fleets with in-route and depot charging strategies.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/mehdi-nourinejad-a0040843_transit-electrification-mta-activity-7200811915428651008-AfOE"}, {"label": "Paper", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_transit-electrification-interactive-operations-activity-7348460897138339840-DyTR"} ] }

{ "title": "Depot Charging Optimization", "yearrole": "2024 · Researcher & Product Developer", "sub": "Depot cost minimization and charging windows", "img": "/images/schoolbus.png", "desc": "Large-scale depot charging optimization with cost control and peak management. I implemented a custom heuristic and moved the compute kernel to WebAssembly for near native performance.", "links": [ {"label": "Demo", "href": "https://lnkd.in/p/g68wNidA"}, {"label": "Collaboration Highlight", "href": "https://lnkd.in/p/gjNjnHwt"} ] }

{ "title": "ResponsePro", "yearrole": "2024 · Lead R&D", "sub": "Fire station planning and response analytics", "img": "/images/responsepro.png", "desc": "Deployment analytics and coverage evaluation for emergency services, already in use by Fire Chiefs in four cities. Includes incident prediction and station deployment optimization, with sensitive call data stored securely on Canadian servers using Google services.", "links": [ {"label": "Tool", "href": "https://interactive-or.net/responsepro"} ] }

{ "title": "Snow PlowingPro", "yearrole": "2024 · Product Developer", "sub": "Arc-routing for winter maintenance", "img": "/images/snow.png", "desc": "Developed with the City of Aurora. Phase 1 delivers optimized arc routing with route design and service level analytics. Phase 2 will add real-time fleet management, storm re-planning, and service-level monitoring.", "links": [ {"label": "Tool Description", "href": "https://interactive-or.net/snowplowpro"} ] }

{ "title": "Real-time Winter Maintenance Monitoring", "yearrole": "2025 · R&D Director", "sub": "Real-time operational tool for monitoring, analysis, and decision-making in winter maintenance", "img": "/images/winter-maintenance.png", "desc": "A real-time operational tool for monitoring, analysis, and decision-making in winter maintenance operations. The system makes critical questions measurable in real time: Where is my snowplow right now? When will it reach my street? How well has each area been plowed by a specific time? This platform provides real-time visibility into plowing and salting operations across entire municipalities, enabling data-driven decision-making for winter maintenance teams and transparent communication with residents about service status.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_aurora-activity-7406837265689194496-LZQw?utm_source=share&utm_medium=member_desktop&rcm=ACoAABTHrvgBk17mK_BgkAwln-q4XYMZo5fgSJw"} ] }

{ "title": "Transit PlanningPro", "yearrole": "2024 · Lead R&D", "sub": "Network edits with KPIs from demand to congestion", "img": "/images/transitplanner.png", "desc": "Interactive four stage workflow covering demand, congestion, network edits, and KPIs. This project is where I learned GTFS Static end to end: ingesting feeds, validating routes, trips, and stops, and turning them into networks and schedules for analysis.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_transit-network-design-and-scheduling-we-activity-7310471035953258496-vr0D"}, {"label": "Tool Description", "href": "https://interactive-or.net/transit"} ] }

{ "title": "Transit Headway Management with AI - Supervisors Version", "yearrole": "2025 · R&D Director", "sub": "AI-powered headway management to reduce bus bunching and gapping", "img": "/images/headway.png", "desc": "A cloud-based, real-time decision-support platform that improves transit service reliability by integrating scheduled service data with live vehicle location updates. The tool maintains a continuously updated digital representation of routes, enabling supervisors to monitor headways, anticipate emerging irregularities, and apply targeted interventions. The system features an AI-guided holding recommendation engine that considers on-board occupancy and real-time headways to restore spacing with minimal passenger impact. It uses self-learning prediction models with what-if analysis to test proposed actions and show expected outcomes. In collaboration with the Toronto Transit Commission (TTC), the tool has been piloted on six routes, with supervisors using AI recommendations to correct 200+ instances of bunching. The system shifts headway management from reactive correction to adaptive, data-guided control that prevents service degradation before passengers experience it.", "links": [ {"label": "White Paper", "href": "https://www.linkedin.com/posts/mehdi-nourinejad-a0040843_whitepaper-ugcPost-7408921412427550721-JeQ2?utm_source=share&utm_medium=member_desktop&rcm=ACoAABTHrvgBk17mK_BgkAwln-q4XYMZo5fgSJw"}, {"label": "Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_ttc-activity-7397735836554813440-mdya?utm_source=share&utm_medium=member_desktop&rcm=ACoAABTHrvgBk17mK_BgkAwln-q4XYMZo5fgSJw"} ] }

{ "title": "Transit Headway Management with AI - Managerial Version", "yearrole": "2025 · R&D Director", "sub": "AI-powered bunching detection, alleviation, and impact forecasting for high-frequency corridors", "img": "/images/headway-managerial.png", "desc": "A strategic optimization platform for alleviating bus bunching in high-frequency corridors through real-time bus holding at stations. The system rebalances service spacing and reduces passenger waiting times by applying AI-driven bunching detection, alleviation strategies, and impact forecasting. The optimization models are headway and occupancy sensitive, prioritizing bunching instances where the trailing bus has lower occupancy than the leading one. Holding times are maintained within industry standards to minimize extended in-vehicle waiting while effectively restoring service regularity.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/mehdi-nourinejad-a0040843_a-best-practice-for-alleviating-bus-bunching-ugcPost-7381767072458080256-zI4z?utm_source=share&utm_medium=member_desktop&rcm=ACoAABTHrvgBk17mK_BgkAwln-q4XYMZo5fgSJw"} ] }

{ "title": "HouseScout", "yearrole": "2025 · R&D Director", "sub": "Real estate decision support combining open data, spatial analysis, and user-defined priorities", "img": "/images/housescout.png", "desc": "HouseScout brings structure and transparency to real estate decision-making by combining open data, spatial analysis, and user-defined priorities in one place. Add properties and workplaces to the map, compare commute times, use customizable multi-criteria scoring for accessibility and schools, analyze nearby amenities with travel-time estimates, visualize X-minute reachability, optimize property visit routes, and explore contextual layers including schools, safety, flood risk, and infrastructure.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/ghazaleh-mohseni-hosseinabadi_realestate-urbananalytics-decisionsupport-activity-7407808924101488640-VkgM?utm_source=share&utm_medium=member_desktop&rcm=ACoAABTHrvgBk17mK_BgkAwln-q4XYMZo5fgSJw"} ] }

{ "title": "Parking Pricing Tool", "yearrole": "2024 · Product Developer", "sub": "Adaptive pricing strategies for curbside management", "img": "/images/parking.png", "desc": "Elasticity-aware curb pricing with policy-driven scenarios.", "links": [ {"label": "Demo", "href": "https://www.linkedin.com/posts/mehdi-nourinejad-a0040843_parking-dynamicpricing-communityimprovement-activity-7225121703939518465-RWt6"} ] }

{ "title": "Vision Zero Tool", "yearrole": "2023 · Product Developer", "sub": "Automated Speed Enforcement camera allocation and cost analysis", "img": "/images/ase.png", "desc": "Allocation strategies for ASE programs targeting high-risk corridors, interactive community safety zones analysis, and cost-aware analysis that considers unit, installation, and operating costs to maximize safety impact per dollar under budget constraints.", "links": [ {"label": "Tool Description", "href": "https://interactive-or.net/ase"} ] }

{ "title": "TSP Game", "yearrole": "2024 · Product Developer", "sub": "Explore and Play the Travel Salesman Problem (TSP)", "img": "/images/tsp.png", "desc": "Teach the complexity of solving traveling salesman problems as the problem size becomes larger - showing the NP-hard nature of the problem.", "links": [] }