🚗Transportation Systems Engineering Unit 2 – Transportation Planning & Demand Modeling

Key Concepts in Transportation Planning

• Transportation planning involves the systematic analysis and design of transportation systems to meet the mobility needs of a community or region
• Aims to optimize the efficiency, safety, and sustainability of transportation networks while considering factors such as land use, economic development, and environmental impacts
• Involves a collaborative process engaging various stakeholders (transportation agencies, local governments, community groups) to identify transportation needs and develop solutions
• Utilizes data-driven approaches to forecast travel demand, assess transportation system performance, and evaluate alternative transportation strategies
• Considers multimodal transportation options including highways, public transit, walking, and cycling to create an integrated and balanced transportation system
• Incorporates principles of equity and accessibility to ensure transportation services are available to all segments of the population, including disadvantaged communities
• Aligns with broader goals and policies related to land use planning, economic development, and environmental sustainability to create livable and sustainable communities

Fundamentals of Travel Demand

• Travel demand refers to the desire or need for individuals to move from one location to another, typically measured in terms of the number of trips or the distance traveled
• Influenced by various factors such as population demographics, land use patterns, economic activities, and transportation system characteristics
• Trip purpose is a key determinant of travel demand, with common categories including work, school, shopping, and recreational trips
• Time of day and day of the week affect travel demand patterns, with peak periods typically occurring during morning and evening commute hours on weekdays
• Socioeconomic factors (income, car ownership, household size) significantly influence travel behavior and demand
• Transportation system attributes such as travel time, cost, comfort, and reliability impact individuals' travel choices and overall demand
• Elasticity measures the responsiveness of travel demand to changes in transportation system attributes or socioeconomic factors (price elasticity, income elasticity)

Data Collection and Analysis Methods

• Data collection is crucial for understanding travel behavior, estimating travel demand, and evaluating transportation system performance
• Household travel surveys gather detailed information on individuals' travel patterns, trip purposes, mode choices, and socioeconomic characteristics
• Traffic counts measure the volume of vehicles or pedestrians passing a specific location, often using automated sensors (loop detectors, video cameras)
• Origin-destination surveys identify the starting and ending points of trips, helping to understand travel patterns and trip distribution
• Passenger surveys collect data on transit ridership, trip purposes, and user demographics to inform transit planning and service improvements
• GPS-based data collection methods (smartphone apps, in-vehicle devices) provide high-resolution spatial and temporal data on travel behavior
• Big data sources (cell phone records, social media, smart card transactions) offer new opportunities for analyzing travel patterns and demand
• Statistical analysis techniques (regression analysis, factor analysis) are used to identify relationships between variables and develop predictive models

Trip Generation and Distribution Models

• Trip generation models estimate the number of trips produced by and attracted to each zone or location based on land use and socioeconomic characteristics
• Production models predict the number of trips originating from each zone, typically based on household characteristics (household size, income, car ownership)
• Attraction models estimate the number of trips destined to each zone, usually based on employment, retail, and other trip-attracting land uses
• Trip rates are derived from empirical data and represent the average number of trips generated per unit of land use or household characteristic
• Trip distribution models allocate the generated trips between origin and destination zones, determining the spatial pattern of travel flows
• Gravity model is a commonly used trip distribution technique that assumes the interaction between zones is proportional to their trip generation and inversely proportional to the distance or travel impedance between them
• Destination choice models predict the probability of choosing a specific destination for a given trip purpose based on the attractiveness and accessibility of alternative destinations
• Iterative proportional fitting (IPF) is a mathematical procedure used to adjust trip matrices to match observed or target marginal totals

Mode Choice and Route Assignment

• Mode choice models predict the probability of individuals choosing a particular transportation mode (car, transit, walking, cycling) for a given trip
• Discrete choice models (logit, probit) are commonly used to analyze mode choice behavior based on the utility or attractiveness of each mode alternative
• Utility functions capture the relative importance of factors influencing mode choice, such as travel time, cost, comfort, and reliability
• Nested logit models account for the hierarchical structure of mode choice decisions, representing the correlation among subsets of alternatives (private vs. public modes)
• Route assignment models allocate travel demand onto the transportation network, determining the flow of vehicles or passengers on specific routes or links
• User equilibrium assignment assumes that travelers choose routes that minimize their individual travel time, resulting in an equilibrium state where no traveler can improve their travel time by unilaterally changing routes
• System optimal assignment aims to minimize the total travel time or cost across the entire network, potentially requiring some travelers to take longer routes for the benefit of the system as a whole
• Stochastic user equilibrium assignment incorporates variability and uncertainty in travel times, accounting for travelers' imperfect knowledge and perception of network conditions
• Dynamic traffic assignment models capture the time-varying nature of traffic flows and congestion, considering the temporal evolution of travel demand and network conditions

Transportation Network Analysis

• Transportation networks are represented as graphs consisting of nodes (intersections, stations) and links (roads, transit lines) with associated attributes (capacity, speed, cost)
• Network topology refers to the spatial arrangement and connectivity of nodes and links, influencing the efficiency and resilience of the transportation system
• Connectivity measures (alpha index, beta index) quantify the degree of connectivity and redundancy in a transportation network
• Shortest path algorithms (Dijkstra's algorithm, A* search) are used to find the minimum-cost paths between origin and destination nodes based on specified criteria (distance, travel time)
• Network flow problems involve optimizing the movement of people, vehicles, or goods through a transportation network subject to capacity constraints and conservation of flow
• Maximum flow problem seeks to determine the maximum amount of flow that can be sent from a source node to a sink node through the network
• Minimum cost flow problem aims to find the flow pattern that minimizes the total cost of transportation while satisfying supply and demand constraints at each node
• Network design problems involve making strategic decisions about the expansion, improvement, or modification of transportation infrastructure to optimize system performance and meet future demand

Emerging Trends and Technologies

• Intelligent Transportation Systems (ITS) integrate advanced technologies (sensors, communication, data analytics) to enhance the efficiency, safety, and sustainability of transportation systems
• Connected vehicles enable communication between vehicles and infrastructure, allowing for real-time information sharing, collision avoidance, and optimized traffic flow
• Autonomous vehicles have the potential to revolutionize transportation by reducing human error, increasing road capacity, and enabling new mobility services (shared autonomous fleets)
• Mobility as a Service (MaaS) platforms integrate various transportation modes and services into a single digital platform, providing users with seamless and personalized mobility options
• Electric vehicles are gaining prominence as a sustainable alternative to fossil fuel-powered vehicles, reducing greenhouse gas emissions and dependence on oil
• Micromobility solutions (e-scooters, bikesharing) offer flexible and convenient options for short trips, complementing traditional public transit and reducing car usage in urban areas
• Big data analytics leverage large-scale data from various sources (sensors, mobile devices, social media) to gain insights into travel behavior, optimize transportation operations, and inform planning decisions
• Smart cities integrate transportation with other urban systems (energy, buildings, public services) to create more livable, efficient, and sustainable urban environments

Real-World Applications and Case Studies

• London's Congestion Charging Scheme: Implemented in 2003, the scheme charges vehicles entering central London during peak hours, reducing traffic congestion and promoting sustainable modes of transportation
• New York City's Select Bus Service (SBS): A bus rapid transit system that combines dedicated bus lanes, off-board fare payment, and transit signal priority to improve bus speed and reliability
• Singapore's Electronic Road Pricing (ERP): A dynamic congestion pricing system that adjusts tolls based on real-time traffic conditions, managing demand and optimizing road network performance
• Portland's Bicycle Infrastructure: The city has invested heavily in bicycle infrastructure, including an extensive network of bike lanes, trails, and bicycle boulevards, resulting in high levels of cycling mode share
• Curitiba's Bus Rapid Transit (BRT) System: Often cited as a model for sustainable urban transportation, Curitiba's BRT system features dedicated bus lanes, high-capacity vehicles, and integrated land use planning
• San Francisco's SFpark: A demand-responsive parking pricing system that adjusts meter rates based on occupancy data, aiming to reduce cruising for parking and improve parking availability
• Barcelona's Superblocks: A urban planning strategy that restricts vehicle access in selected areas, creating pedestrian-friendly spaces and promoting active transportation modes
• Dutch Cycling Culture: The Netherlands is renowned for its extensive cycling infrastructure and high levels of bicycle usage, supported by policies, land use patterns, and cultural norms that prioritize cycling as a mode of transportation