UNIT-I URBAN TRANSPORTATION PROBLEMS & TRAVEL DEMAND

Urban transportation planning must consider various travel characteristics to address challenges
like congestion, pollution, and safety. Key aspects include understanding travel demand, trip
patterns, modal choices, and the impact of land use on transportation. Effective planning requires
integrating these factors with land use planning and addressing issues like non-motorized transport
and equity.

Urban Transport Issues:

 Traffic Congestion:
High traffic volume exceeding road capacity leads to delays, increased travel times, and
reduced efficiency.
 Public Transport Challenges:
Inadequate public transport, especially during peak hours, can lead to overcrowding, long
waiting times, and reduced service quality.
 Non-Motorized Transport (NMT) Issues:
Lack of safe and convenient pedestrian and cycling infrastructure can discourage NMT,
leading to increased reliance on cars and negative environmental impacts.
 Parking Problems:
Limited parking spaces in urban areas can cause congestion and make it difficult for people
to access destinations.
 Environmental Impacts:
Motor vehicle emissions contribute to air and noise pollution, impacting public health and the
environment.
 Safety Concerns:
Increased traffic volume and congestion can lead to more accidents and injuries.
 Equity Issues:
Lack of access to transportation options can disproportionately affect low-income individuals
and those with disabilities.
 Land Use-Transportation Integration:
Inadequate integration of land use and transportation planning can exacerbate transportation
problems, leading to longer trips and increased travel demand.

1
Travel Characteristics:
Travel Demand:
Understanding the total volume of trips, spatial and temporal variations, and the factors
influencing travel demand is crucial for planning.
 Trip Patterns:
Analyzing trip origins, destinations, and purposes (work, leisure, etc.) helps in identifying
travel patterns and needs.
 Modal Choice:
Understanding why people choose specific modes of transportation (car, public transit,
walking, cycling) is important for developing appropriate transport strategies.
 Travel Time and Cost:
Considering travel time, cost, and comfort levels influences modal choice and overall
satisfaction with the transportation system.
 User Characteristics:
Factors like age, income, and physical abilities influence travel behavior and accessibility
needs.
Evaluation of planning process or overall planning process:
The evaluation of urban transportation planning involves assessing transportation options to meet
current and future needs, considering factors like costs, travel demand, and community
impact. This process typically involves several key steps: data collection, goal setting, alternative
development, cost and demand estimation, evaluation, and project selection. Ultimately, the goal
is to create a sustainable, efficient, and equitable transportation system.

1. Data Collection and Analysis:

 This phase involves gathering information on existing transportation infrastructure, travel
patterns, demographics, land use, and economic conditions.
 Data is used to understand current travel behavior and to build models that can forecast
future travel demand.
 Examples of data collected include origin-destination studies, traffic volume studies, and
speed studies.
2. Goal Setting:
 Establish clear goals and objectives for the transportation system, including desired levels
of service, safety, and accessibility.

2
  These goals should be aligned with broader community and land use objectives.
3. Alternative Generation:
 Develop a range of alternative transportation solutions, considering different modes,
routes, and technologies.
 These alternatives may include public transit improvements, road expansions, pedestrian
and bicycle infrastructure, and intelligent transportation systems.
 The planning process may also involve analyzing the effects of different land-use plans
and lifestyle scenarios.
4. Cost and Demand Estimation:
 Estimate the costs associated with each alternative, including construction, operation, and
maintenance.
 Forecast travel demand for each alternative, using travel demand models that consider
factors like population growth, economic activity, and land use patterns.
 Evaluate the impacts of each alternative on different groups of people, including those who
benefit and those who may be negatively impacted.
5. Evaluation:
 Compare and evaluate the alternatives based on a range of criteria, including cost, travel
time, safety, environmental impact, and accessibility.
 Use a combination of quantitative and qualitative methods to assess the performance of
each alternative.
 Consider factors like passenger comfort, door-to-door service, and the social equity of the
transportation system.
6. Project Selection:
 Based on the evaluation, select the preferred transportation project or set of projects.
 The selection process should be transparent and involve input from stakeholders.
7. Implementation and Monitoring:
 Implement the selected project and monitor its performance over time.
 Make adjustments as needed based on monitoring results and changing conditions.

3
System approach:
The systems approach in urban transportation planning involves analyzing the complex
interactions between transportation supply and demand as interconnected components of a larger
system. This approach considers the entire transportation network, including infrastructure,
services, and travel behavior, to develop solutions that effectively address urban mobility
challenges.

aspects of a systems approach in urban transportation planning:

 Interconnectedness:
Transportation supply (infrastructure, services) and demand (travel needs) are viewed as
interdependent parts of a single system.
 Equilibrium:
The goal is to achieve a balance between supply and demand, ensuring efficient and
sustainable mobility.
 Modeling and Simulation:
Mathematical models and simulations are used to analyze the system's behavior under
different scenarios and to predict the impacts of various policy interventions.
 Data-Driven Decisions:
The systems approach relies on data collection and analysis to understand travel patterns,
identify bottlenecks, and evaluate the effectiveness of implemented solutions.
 Comprehensive Solutions:
It considers a wide range of factors, including land use, demographics, and economic
conditions, to develop holistic and integrated transportation plans.

4
5
WORKING:
1. Understanding Travel Demand:
Analyzing travel patterns, including trip origins and destinations, purposes, modes, and
timing, is crucial for understanding the needs of the population.
2. Evaluating Supply:
Assessing the capacity and performance of the existing transportation network, including
roads, public transit, and other modes, is essential.
3. Identifying Imbalances:
Comparing supply and demand to identify areas where there are shortages or surpluses of
transportation capacity.
4. Developing Strategies:
Formulating strategies to address imbalances, which may include expanding infrastructure,
improving public transit services, implementing traffic management techniques, or
influencing travel behavior through pricing or other measures.
5. Modeling and Simulation:
Using computer models to simulate the effects of different strategies on the transportation
system and to optimize the selection of solutions.
6. Monitoring and Evaluation:
Continuously monitoring the performance of the transportation system and evaluating the
effectiveness of implemented strategies to ensure continuous improvement.
Example: In Chennai, India, a system dynamics model was developed to simulate the interaction
between transport demand and supply. This model was used to test different policy scenarios, such
as expanding public transit or implementing congestion pricing, and to identify sustainable
transportation solutions for the city.

USES OF SYSTEMS APPROACH:

 More efficient transportation:

By optimizing the allocation of resources, the systems approach can lead to a more efficient
and cost-effective transportation system.
 Reduced congestion:
By balancing supply and demand and implementing strategies to manage traffic, congestion
can be reduced.

6
 Improved air quality:
By promoting the use of public transit and other sustainable modes, air quality can be
improved.
 Enhanced accessibility:
By ensuring that transportation services are available to all, accessibility can be improved,
particularly for vulnerable populations.
 More sustainable urban development:
By integrating transportation planning with land use planning, the systems approach can
contribute to more sustainable urban development patterns.
Long-Term vs. Short-Term Planning:
 Short-term planning
focuses on quick solutions and improvements, often within a 5-year time frame. These plans
typically involve traffic engineering solutions, such as signal timing adjustments, one-way
streets, or parking management. The goal is to provide immediate relief to transportation
bottlenecks and improve system efficiency.
 Short-term solutions
might offer quick fixes but could be less effective in the long run if they don't address
underlying issues.
 Long-term planning
takes a broader, more strategic approach, with a timeframe of 10-25 years or more. These
plans involve major infrastructure projects, like new roadways, public transit systems, or
large-scale land-use changes. Long-term planning aims to address future needs,
accommodate growth, and shape the overall urban environment.
 Long-term plans
can be costly and time-consuming to implement, and they may face uncertainties related to
future conditions and unforeseen events.

Considerations:

 Public Participation:
Engaging the public throughout the planning process is essential to ensure that plans reflect
community needs and preferences.
 Integration:
Urban transportation planning should be integrated with land-use planning, environmental
considerations, and other relevant policies.

7
 Technology:
New technologies like intelligent transportation systems (ITS) can play a role in both short-
term and long-term planning.

Demand function: In urban transportation planning, a demand function describes the relationship
between the quantity of travel demanded and various factors influencing that demand, such as cost,
income, and land use. It helps predict how many trips will be made, by what mode, and where,
which is crucial for developing effective transportation systems.

 Travel Demand:
The need for people to travel, arising from various factors like location of homes and
workplaces, and the desire to access services and activities.
 Expressed as:
The number of trips or person-trips (or vehicle trips) that are expected on a transportation
network.
 Influenced by:
Land use patterns, socio-economic conditions, and the performance of the transportation
system itself.
Demand Function:

 Mathematical Representation:
A demand function is a mathematical expression (equation or model) that shows how the
quantity of trips demanded changes in response to changes in factors that influence travel
behavior.
 Core Components:
It typically relates the quantity of trips to:
o Cost of travel: Includes factors like fares, tolls, fuel costs, and travel time.
o Income: Higher income generally leads to higher travel demand.
o Land Use: The distribution of homes, workplaces, and amenities affects travel
patterns.
o Other factors: Demographic characteristics (age, household size), and the availability
and quality of different transportation modes.
 Types of Demand Functions:
o Trip Generation: Predicts the total number of trips originating in a specific zone.

o Trip Distribution: Predicts the destinations of those trips.

8
 o Mode Choice: Predicts the mode of transportation used (car, bus, train, etc.).
o Traffic Assignment: Predicts how trips will be distributed across the transportation
network.
Importance Of Demand Function:

 Predicting Future Demand:

Helps planners anticipate future travel needs and plan infrastructure accordingly.
 Evaluating Policies:
Allows for the assessment of the impact of transportation policies (e.g., new bus routes, road
pricing) on travel behavior.
 Optimizing System Performance:
Helps in designing an efficient and balanced transportation system that meets the needs of
users.
 Resource Allocation:
Guides the allocation of resources for infrastructure development and transportation services.
 Land Use Planning:
Informs land use decisions by understanding the relationship between land use patterns and
travel demand.

Independent variables: Travel attributes are considered independent variables because they
are the factors that influence people's decisions about travel. These decisions are then reflected in
the dependent variables, such as the number of trips generated, the chosen mode of transport, and
the routes taken.

1.Travel Time: This refers to the time it takes to complete a trip, including walking, waiting,
and in-vehicle time.
2.Travel Cost: This includes all expenses associated with a trip, such as fares, fuel costs,
parking fees, and tolls.
3.Travel Frequency: This refers to how often a particular mode of transport is
available. Higher frequency can make a mode more attractive, particularly for public
transport.
4.Travel Comfort: This encompasses factors like space, seating availability, noise levels,
and the overall pleasantness of the journey.
5.Travel Safety: This includes the perceived and actual safety of a mode of transport,
considering factors like accident rates and personal security.

9
Relationship with Travel Demand:

1.Trip Generation:
Travel attributes influence how many trips are generated in a particular area. For example, if
a new highway reduces travel times to a certain area, it may lead to an increase in trip
generation to that area.
2.Mode Choice:
Travel attributes play a significant role in determining which mode of transport is chosen. If
a new public transport option offers faster and more convenient travel than driving, it may
attract more users.
3.Traffic Assignment:
Travel attributes affect how traffic is distributed across the transportation network. Travelers
will tend to choose routes that offer the shortest travel times, lowest costs, or best level of
service.
Assumption in demand estimation:
1) Future travel demand is influenced by land use (population and economic activity) and
associated trip generation.
2) Trip distribution, mode choice, and route assignment can be modeled using mathematical
relationships and historical data.
3) Travel demand is predictable based on factors like population, employment, income, and travel
times.
4) Individuals make rational choices to maximize their utility (satisfaction) when choosing modes
and routes.
5) Transportation system improvements can be modeled to affect travel behavior and demand.

1. Land Use and Socioeconomic Factors:

a. Future land use drives travel:

It's assumed that future travel demand is largely determined by how land is used (residential,
commercial, industrial) and the associated population and economic activity.
b. Predictable population and employment:
Planners forecast future population and employment figures for each traffic analysis zone
(TAZ) and assume these are key drivers of travel demand.
c.Relationship between land use and travel:
It's assumed that land use patterns (e.g., residential density, employment centers) influence
the number of trips generated and the destinations of those trips.

10
2. Travel Behavior and Choice:

a. Rational decision-making:
Individuals are assumed to make choices (e.g., mode of transport, route) that maximize their
personal utility, often measured by factors like travel time, cost, and comfort.
b. Trip generation, distribution, and mode choice:
1.Trip Generation: Predicts the total number of trips originating in a specific zone.
2.Trip Distribution: Predicts the destinations of those trips.
3.Mode Choice: Predicts the mode of transportation used (car, bus, train, etc.).
4.Traffic Assignment: Predicts how trips will be distributed across the transportation network.

3. Network and System Behavior:

a. Network capacity and congestion:
Models assume that the capacity of the transportation network (roads, transit lines) and the
level of congestion can be represented mathematically.
b. Travel time and cost:
Assumptions are made about how travel times and costs vary based on distance, congestion,
and mode of transport.
c.System equilibrium:
In some models, it's assumed that the transportation system will reach a state of equilibrium
where travel patterns are stable.
4. Data and Model Limitations:
a. Historical data as a basis:
Travel demand models rely heavily on historical data to calibrate and validate their
parameters.
b. Model limitations:
It's important to acknowledge that travel demand models are simplifications of complex
human behavior and transportation systems, and they have inherent limitations.

11
In urban transportation planning, demand estimation involves forecasting future travel
patterns. Sequential and simultaneous approaches are two main methods used to model these
patterns. Sequential models estimate trip generation, distribution, modal split, and traffic
assignment one after the other, while simultaneous models estimate these elements
concurrently. Assumptions are crucial in both approaches, and they often involve socio-economic
factors, land use characteristics, and transportation network attributes.

Sequential Approach:
1.Step-by-step process:
This approach involves breaking down travel demand into a series of discrete steps: trip
generation (how many trips are produced and attracted), trip distribution (where those trips
are going), modal split (which mode of transport is used), and traffic assignment (how those
trips utilize the transportation network).
2.Independent estimation:
Each step is treated as an independent model, and the output of one step becomes the input
for the next.
3.Assumptions:
This approach often assumes that trip generation is independent of other decisions, and that
modal split is independent of the specific route chosen.
Simultaneous Approach:
1.Integrated estimation:
Instead of separate steps, this approach models all aspects of travel demand (generation,
distribution, mode choice, and assignment) simultaneously.
2.Interdependencies:
It considers the interdependencies between different decisions (e.g., how the choice of mode
affects the routes people take).
3.Assumptions:
Simultaneous models often assume that individuals make choices considering all aspects of
their trip at once.
4.Complexity:
Simultaneous models are more complex to develop and calibrate, but they may offer a more
realistic representation of travel behavior.

12
Aggregate Techniques:
 Definition:
Aggregate models analyze travel demand at a zonal or group level, using summary statistics
to represent the behavior of a larger population.
 Data:
These models often rely on aggregated data, such as trip generation rates per zone, average
travel times between zones, or population density.
 Examples:
Gravity models, aggregate logit models, and regression models are commonly used aggregate
techniques.
 Strengths:
Relatively simple to develop and apply, require less data, and computationally efficient.
 Weaknesses:
May not capture the heterogeneity of travel behavior, may be less accurate in predicting
individual choices, and can be sensitive to the chosen aggregation level.
Disaggregate Techniques:
 Definition:
Disaggregate models analyze travel behavior at the individual level, considering the
characteristics and choices of each traveler.
 Data:
These models require detailed data on individual travel behavior, such as trip purpose, mode
choice, income, and demographic characteristics.
 Examples:
Discrete choice models (logit, probit), activity-based models, and microsimulation models
are commonly used disaggregate techniques.
 Strengths:
Can capture the heterogeneity of travel behavior, provide more accurate predictions of
individual choices, and are less sensitive to the chosen aggregation level.
 Weaknesses:
More data-intensive, computationally demanding, and can be more complex to develop and
calibrate

13
Comparison:
Feature Aggregate Techniques Disaggregate Techniques

Level of Analysis Zonal or group level Individual level

Data Less data, summary statistics More data, individual characteristics

Requirements

Computational Less complex, More complex, computationally demanding

Complexity computationally efficient

Accuracy May not capture Captures heterogeneity, more accurate in

heterogeneity, less accurate in predicting individual choices
predicting individual choices

Sensitivity to Sensitive to the chosen Less sensitive to the chosen aggregation level
Aggregation aggregation level

Examples Gravity models, aggregate Discrete choice models, activity-based

logit models models, microsimulation models

14