Applied Data Science for Transport Network Planning Training Course  

Course Introduction

The Applied Data Science for Transport Network Planning Training Course provides a comprehensive framework for applying data science, statistical analysis, machine learning, geospatial analytics, and predictive modeling to modern transportation planning challenges. Participants learn how to transform complex transport datasets into actionable intelligence for network design, demand forecasting, capacity planning, congestion management, and infrastructure investment decisions.

The course examines how data from traffic sensors, public transport systems, GPS devices, mobile applications, ticketing platforms, road networks, travel surveys, vehicle fleets, and geographic information systems can be integrated to create reliable analytical foundations. Participants develop practical approaches for cleaning, validating, enriching, and analyzing multimodal transport data while addressing missing observations, inconsistent records, spatial dependencies, temporal patterns, and changing travel behavior.

Participants explore advanced methods for understanding travel demand and predicting how passenger and vehicle movements respond to population growth, land-use changes, economic activity, infrastructure development, pricing policies, service changes, and emerging mobility technologies. Statistical techniques and machine learning models are applied to estimate origin-destination patterns, trip generation, route choice, traffic volumes, public transport demand, peak-period conditions, and network utilization.

The course also focuses on geospatial and network-based analytics for evaluating transportation corridors, intersections, routes, terminals, transit networks, and multimodal connections. Learners examine how spatial data, graph-based methods, accessibility measures, travel-time analysis, network centrality, clustering, and visualization techniques can support evidence-based decisions about infrastructure improvements, service coverage, routing, and connectivity.

A major component of the program addresses scenario analysis, simulation, optimization, and predictive decision support. Participants learn how data science can be used to test alternative network configurations, forecast future transport conditions, identify bottlenecks, evaluate interventions, optimize resource allocation, and assess the potential effects of transport policies under different demographic, economic, environmental, and operational scenarios.

By completing the Applied Data Science for Transport Network Planning Training Course, professionals will be better equipped to establish data-driven transport planning workflows that connect analytical models with practical planning decisions. The program emphasizes reproducibility, model validation, stakeholder communication, ethical data use, uncertainty management, and the translation of analytical findings into transport strategies that can support resilient, accessible, and efficient mobility systems.

Duration

10 days

Who Should Attend

• Transport planners responsible for network development, corridor planning, mobility analysis, and long-term transportation strategy.

• Traffic engineers seeking advanced analytical methods for understanding congestion, capacity, travel demand, and network performance.

• Public transport planners analyzing passenger flows, service coverage, route performance, and transit network requirements.

• Data scientists working with transportation, mobility, geospatial, or infrastructure datasets and analytical applications.

• Urban planners evaluating the relationship between land use, population growth, accessibility, and transportation demand.

• GIS professionals developing spatial models and geospatial decision-support systems for transport planning activities.

• Infrastructure planners assessing road, rail, transit, terminal, interchange, and multimodal network investment requirements.

• Transport economists applying quantitative methods to evaluate demand, investment scenarios, pricing, and network performance.

• Mobility and smart-city professionals working with connected transport, intelligent mobility, and urban movement datasets.

• Government officials involved in transportation policy, infrastructure planning, public mobility programs, or network investment decisions.

• Consulting professionals delivering transport studies, demand forecasts, feasibility assessments, and network optimization projects.

• Public transport operators seeking data-driven approaches to service planning, capacity management, and route optimization.

• Logistics and fleet planning professionals analyzing road networks, travel patterns, routing requirements, and transportation efficiency.

• Infrastructure investment and project management professionals requiring analytical evidence for transport planning and prioritization decisions.

Course Objectives

• Develop practical expertise in applying data science techniques to transportation planning, network analysis, demand forecasting, infrastructure assessment, and mobility decision-making.

• Understand how to collect, integrate, clean, validate, and structure multimodal transportation datasets from sensors, surveys, GPS systems, ticketing platforms, GIS databases, and operational systems.

• Apply statistical and machine learning techniques to estimate travel demand, traffic volumes, passenger flows, trip patterns, route utilization, and changes in network behavior.

• Build analytical workflows that combine temporal, spatial, demographic, socioeconomic, and operational variables to explain transportation demand and network performance.

• Use geospatial analytics and network modeling methods to evaluate accessibility, connectivity, travel times, bottlenecks, route structures, service coverage, and transportation inequalities.

• Develop forecasting models capable of supporting short-term traffic prediction and longer-term transportation planning under population, land-use, economic, and infrastructure scenarios.

• Analyze origin-destination patterns and travel behavior using structured datasets, spatial methods, probabilistic techniques, and machine learning approaches.

• Apply optimization methods to transportation network design, route planning, capacity allocation, service scheduling, infrastructure prioritization, and resource deployment.

• Evaluate alternative transport scenarios using simulation, predictive analytics, sensitivity analysis, uncertainty assessment, and performance indicators.

• Design dashboards and analytical visualizations that communicate transport network conditions, trends, forecasts, risks, and planning alternatives to technical and non-technical stakeholders.

• Establish responsible data science practices covering privacy, governance, model transparency, data quality, bias assessment, reproducibility, and appropriate interpretation of analytical results.

• Translate data science outputs into practical transport planning recommendations, investment evidence, operational strategies, and measurable network performance improvement initiatives.

Comprehensive Course Outline

Module 1: Foundations of Data Science for Transport Planning

• Principles of applying data science to transport network planning, mobility strategy, infrastructure analysis, and evidence-based decision-making.

• Major transportation datasets, their characteristics, analytical uses, limitations, collection methods, and quality considerations.

• Relationship between transportation systems, land use, demographics, economic activity, infrastructure, and observed travel behavior.

• Establishing transport analytics workflows that connect business questions, datasets, analytical methods, models, validation, and planning decisions.

Module 2: Transport Data Collection and Data Engineering

• Methods for collecting traffic, passenger, vehicle, GPS, ticketing, survey, roadway, and public transport operational data.

• Data integration approaches for combining structured, semi-structured, geospatial, streaming, and historical transportation datasets.

• Techniques for identifying missing records, duplicate observations, inconsistent timestamps, erroneous coordinates, and abnormal transportation measurements.

• Designing reusable transport data pipelines that support repeatable analysis, monitoring, forecasting, visualization, and network planning activities.

Module 3: Exploratory Transport Data Analysis

• Exploratory analysis of traffic volumes, passenger movements, travel times, speeds, trip frequencies, and network utilization patterns.

• Statistical techniques for identifying temporal trends, peak periods, seasonal effects, anomalies, and recurring transportation demand behavior.

• Feature engineering methods for transforming raw transportation observations into useful variables for statistical and machine learning models.

• Analytical approaches for comparing transport performance across corridors, zones, routes, time periods, vehicle classes, and passenger segments.

Module 4: Geospatial Analytics for Transportation Networks

• Geographic data structures, coordinate systems, spatial joins, network layers, and geospatial workflows used in transportation planning.

• Spatial analysis techniques for measuring accessibility, proximity, connectivity, service coverage, travel distance, and network reach.

• Applying GIS-based analytical methods to roads, railways, transit routes, intersections, terminals, stations, and multimodal facilities.

• Developing transport maps and spatial visualizations that reveal network gaps, bottlenecks, demand concentrations, and underserved locations.

Module 5: Transportation Network Modeling and Graph Analytics

• Representing transportation systems as graphs containing nodes, links, routes, intersections, stations, transfers, and network relationships.

• Applying graph algorithms to shortest paths, connectivity, centrality, route structure, network resilience, and transportation accessibility.

• Analyzing network topology to identify critical links, alternative routes, vulnerable connections, and potential capacity constraints.

• Combining graph analytics with transportation demand and operational data to support network design and infrastructure planning decisions.

Module 6: Travel Demand Analysis and Forecasting

• Principles of travel demand analysis, including trip generation, trip distribution, mode choice, route selection, and network assignment concepts.

• Statistical and machine learning approaches for forecasting passenger demand, vehicle movements, traffic volumes, and transport service utilization.

• Developing explanatory variables using population, employment, land use, economic activity, weather, events, pricing, and infrastructure conditions.

• Evaluating forecast accuracy using appropriate validation strategies, error measures, scenario assumptions, and uncertainty analysis.

Module 7: Origin-Destination and Mobility Pattern Analysis

• Understanding origin-destination matrices and their role in transport demand estimation, network planning, and infrastructure investment.

• Techniques for deriving movement patterns from travel surveys, GPS observations, mobile mobility datasets, ticketing records, and transportation models.

• Methods for estimating missing or incomplete origin-destination information while maintaining analytical consistency and appropriate uncertainty controls.

• Visualizing travel flows to identify major corridors, mobility clusters, transfer points, demand concentrations, and changing movement patterns.

Module 8: Traffic Flow and Congestion Analytics

• Analytical methods for studying traffic flow, speed variation, congestion formation, queue behavior, travel-time reliability, and roadway performance.

• Developing predictive models for traffic conditions using historical observations, real-time data, weather, incidents, events, and network characteristics.

• Identifying recurring and non-recurring congestion patterns and evaluating their relationship with network capacity and operational conditions.

• Using data-driven indicators to assess congestion severity, delay, reliability, bottleneck locations, and potential intervention opportunities.

Module 9: Public Transport Network Analytics

• Analyzing passenger demand, boarding patterns, alighting behavior, route utilization, transfer activity, and service coverage across public transport systems.

• Applying data science to timetable analysis, route performance, passenger loads, service reliability, and capacity-demand alignment.

• Identifying opportunities for route restructuring, service frequency adjustments, interchange improvements, and network connectivity enhancements.

• Developing performance indicators for evaluating public transport accessibility, reliability, utilization, passenger experience, and network effectiveness.

Module 10: Machine Learning for Transport Planning

• Supervised learning techniques for predicting traffic demand, passenger volumes, travel times, network conditions, and transportation performance indicators.

• Unsupervised learning approaches for identifying mobility clusters, travel behavior segments, route groups, demand zones, and unusual transportation patterns.

• Feature selection, model training, cross-validation, hyperparameter optimization, and performance assessment for transport machine learning applications.

• Interpreting machine learning results and communicating model limitations, uncertainty, assumptions, and practical implications to planning stakeholders.

Module 11: Transport Network Optimization

• Optimization concepts for transportation network design, capacity allocation, routing, service planning, and infrastructure resource distribution.

• Applying mathematical programming and heuristic approaches to identify feasible network improvements under operational and budget constraints.

• Optimizing route structures, transit services, infrastructure priorities, vehicle allocation, and network capacity using defined performance objectives.

• Conducting sensitivity and trade-off analysis to understand how alternative constraints and objectives affect transportation planning solutions.

Module 12: Scenario Planning and Transport Simulation

• Designing transport scenarios that incorporate population growth, land-use change, infrastructure investment, economic development, policy changes, and mobility trends.

• Using simulation and predictive models to estimate network responses under alternative transport demand and infrastructure conditions.

• Comparing baseline and intervention scenarios through travel time, accessibility, congestion, capacity, reliability, and service performance indicators.

• Incorporating uncertainty, sensitivity analysis, and alternative assumptions into scenario-based transport planning and investment assessments.

Module 13: Data Visualization and Transport Planning Dashboards

• Designing effective transportation dashboards that communicate network performance, demand patterns, congestion, accessibility, and forecast information.

• Developing interactive maps, charts, indicators, and spatial visualizations for monitoring transport conditions and supporting planning decisions.

• Selecting appropriate visualization techniques for technical analysts, transport managers, policymakers, infrastructure planners, and community stakeholders.

• Establishing dashboard data pipelines that maintain consistent definitions, refresh schedules, validation processes, and traceable analytical outputs.

Module 14: Transport Infrastructure Investment and Prioritization

• Using data-driven methods to identify infrastructure deficiencies, capacity constraints, accessibility gaps, and network improvement requirements.

• Developing quantitative frameworks for comparing transport investment scenarios using demand, network performance, accessibility, cost, and risk indicators.

• Applying predictive analytics to estimate potential network impacts associated with roads, rail systems, transit corridors, stations, terminals, and interchanges.

• Creating evidence-based prioritization frameworks that connect analytical findings with project planning, budgeting, monitoring, and performance evaluation.

Module 15: Responsible and Resilient Transport Data Science

• Addressing privacy, security, governance, ethical use, and responsible handling of personal and mobility-related transportation data.

• Assessing model bias, data representativeness, uncertainty, and potential distributional effects in data-driven transportation planning.

• Applying resilience analytics to understand network vulnerability, disruption impacts, alternative routing, recovery capacity, and critical infrastructure dependencies.

• Establishing reproducible analytical practices that support transparent assumptions, auditable models, reliable results, and responsible planning decisions.

Module 16: Integrated Transport Network Planning Capstone

• Developing an end-to-end data science project that integrates transport data preparation, exploratory analysis, geospatial modeling, forecasting, and network evaluation.

• Building a practical transport planning model addressing demand forecasting, congestion, accessibility, public transport performance, or infrastructure requirements.

• Testing alternative planning scenarios and presenting analytical results through maps, dashboards, visualizations, performance indicators, and decision-support outputs.

• Presenting a complete evidence-based transport network planning strategy with documented assumptions, model limitations, implementation considerations, and monitoring measures

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.