2.2 Trip generation and distribution models

Trip generation and distribution models are crucial tools in transportation planning. They estimate the number of trips produced and attracted by different zones, considering factors like land use, socioeconomic data, and network characteristics. These models form the foundation for understanding travel patterns and demand.

The process involves two key steps: trip generation, which calculates trips for each zone, and trip distribution, which allocates trips between origins and destinations. Various methods, from simple regression to complex gravity models, are used to capture the intricacies of travel behavior and create accurate forecasts for planning purposes.

Trip generation modeling

Purpose and methods

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• First step in four-step transportation planning process estimates number of trips produced by and attracted to each zone in study area
• Quantifies relationship between land use characteristics and number of trips generated
• Uses socioeconomic data, land use information, and transportation network characteristics as input variables
• Common methods include:
  • Regression analysis correlates trip generation with explanatory variables
  • Cross-classification analysis categorizes trip rates based on multiple variables
  • Category analysis groups similar land uses to determine average trip rates
• Developed separately for different trip purposes (home-based work, home-based other, non-home-based trips)
• Output expressed as trip rates or total trips per zone serves as input for subsequent planning steps
• Accounts for temporal variations (peak hour, daily, seasonal patterns) to accurately represent travel demand

Model components and considerations

• Incorporates various factors influencing trip generation:
  • Household characteristics (income, vehicle ownership, family size)
  • Employment data (number of jobs, type of industry)
  • Land use attributes (residential density, commercial floor space)
  • Accessibility measures (proximity to transit, walkability)
• Addresses special generators requiring separate treatment:
  • Airports generate unique travel patterns based on flight schedules
  • Universities create distinct trip patterns tied to academic calendars
  • Shopping malls produce trips influenced by retail hours and events
• Considers emerging trends affecting trip generation:
  • E-commerce impact on shopping trips
  • Telecommuting influence on work trips
  • Ride-sharing services effect on personal vehicle trips

Production vs Attraction

Defining production and attraction

• Trip production represents number of trips originating from a zone (typically residential areas)
• Trip attraction signifies number of trips destined for a zone (usually employment or commercial areas)
• Production and attraction not always equal for a given zone as trips may cross zone boundaries
• Key differences in modeling approach:
  • Production models focus on household-level characteristics
  • Attraction models emphasize destination-specific attributes
• Balancing production and attraction crucial for model consistency:
  • Ensures total trips produced match total trips attracted in study area
  • May require adjustments to reconcile differences between production and attraction estimates

Modeling techniques and variables

• Trip production models often utilize:
  • Household income (higher income generally correlates with more trips)
  • Car ownership (more vehicles typically lead to increased trip-making)
  • Household size (larger households tend to generate more trips)
  • Examples:
    • Trips = 1.5 + 0.3 * (Household Size) + 0.2 * (Vehicles Owned)
    • Cross-classification table based on income and household size
• Trip attraction models typically rely on:
  • Employment data (number of jobs by industry type)
  • Commercial floor space (retail square footage correlates with shopping trips)
  • Other destination-specific attributes (school enrollment, hotel rooms)
  • Examples:
    • Trips = 3.0 * (Number of Employees) + 0.1 * (Retail Floor Space in sq ft)
    • Attraction rates per 1000 sq ft for different land use categories

Trip distribution models

Gravity model fundamentals

• Allocates trips between origin and destination zones creating origin-destination (O-D) matrix
• Based on principle that trip interchange between zones directly proportional to their trip generation and inversely proportional to travel impedance
• Gravity model formula: $T_{ij} = P_i * A_j * F_{ij} * K_{ij}$
  • $T_{ij}$: Trips from zone i to zone j
  • $P_i$: Productions from zone i
  • $A_j$: Attractions to zone j
  • $F_{ij}$: Friction factor between zones i and j
  • $K_{ij}$: Socioeconomic adjustment factor (optional)
• Friction factor represents impedance to travel between zones:
  • Typically expressed as function of travel time, distance, or generalized cost
  • Common forms include negative exponential, power function, or gamma function
  • Example: $F_{ij} = e^{-β * t_{ij}}$ where $t_{ij}$ is travel time between zones
• Calibration involves adjusting model parameters to match observed travel patterns:
  • Uses survey data or traffic counts for validation
  • Iterative process to fine-tune friction factors and adjustment factors

Alternative distribution methods

• Growth factor methods (Fratar method) project future trip patterns based on existing travel behavior:
  • Simple to apply but limited in predicting changes due to new developments
  • Example: Future trips = Current trips * (Future total trips / Current total trips)
• Destination choice models based on discrete choice theory:
  • Incorporate wide range of variables influencing trip distribution
  • Can capture complex decision-making processes
  • Example: Multinomial logit model for choosing between multiple destinations
• Intervening opportunities models consider spatial arrangement of opportunities:
  • Assumes trips are terminated at the first acceptable destination encountered
  • May offer more realistic patterns in certain contexts
  • Example: Shopping trips distributed based on proximity and size of retail centers
• Entropy maximization models provide theoretical framework:
  • Maximize entropy of trip distribution subject to known constraints
  • Can incorporate multiple factors influencing travel patterns
  • Example: Maximize entropy while matching observed trip length distribution

Trip distribution techniques

Model comparison and selection

• Gravity models widely used due to simplicity and intuitive nature:
  • Strengths: Easy to implement, computationally efficient
  • Weaknesses: May oversimplify complex behaviors, struggle with long-distance trips
• Growth factor methods computationally efficient and preserve existing patterns:
  • Strengths: Simple to apply, maintain base year travel behavior
  • Weaknesses: Cannot predict changes due to new developments or system changes
• Destination choice models incorporate wide range of variables:
  • Strengths: Can capture complex decision-making, flexible specification
  • Weaknesses: Require extensive data, more complex to calibrate and implement
• Intervening opportunities models consider spatial arrangement:
  • Strengths: Account for competing destinations, may better represent certain trip types
  • Weaknesses: Difficult to calibrate, may not capture all factors influencing distribution
• Model effectiveness depends on quality and availability of input data:
  • Accurate travel time or cost information between zones crucial
  • Socioeconomic data at appropriate spatial resolution important

Advanced techniques and future directions

• Activity-based models simulate individual daily activity patterns:
  • Represent trips as part of broader activity schedules
  • Can capture complex interactions between trips and activities
  • Example: Modeling a person's entire day including work, shopping, and leisure trips
• Agent-based simulations model individual travelers' decisions:
  • Can incorporate heterogeneity in traveler characteristics and preferences
  • Allow for emergent behavior and system-level outcomes
  • Example: Simulating traffic patterns by modeling each vehicle as an agent
• Integration with big data sources enhances model accuracy:
  • Use of mobile phone data to validate and calibrate trip distribution patterns
  • Incorporation of real-time traffic data to update travel times dynamically
• Machine learning approaches for trip distribution:
  • Neural networks to predict O-D flows based on multiple input features
  • Random forests to classify trips into different distribution patterns
• Challenges in implementing advanced techniques:
  • Require significant computational resources and expertise
  • Data privacy concerns when using individual-level information
  • Balancing model complexity with practical applicability for planning agencies