Traffic Data Collection, Statistical Distributions and Performance

Learning objectives

Learn to characterize the mechanisms, assumptions and observational evidence connecting traffic counting and peak-hour factor, travel time, speed surveys and reliability, capacity, bottlenecks and level of service, origin–destination studies and sampling. Demonstrate unit consistency, interpret safety and access alongside vehicle throughput, and compare model predictions against field observations before making recommendations.

How to study this unit

Work through all four lessons, reproduce each numerical illustration without copying the result, operate the linked investigations, then answer the unit assessment and prepare a short project memo. Recognize which assumptions are useful for learning and which prevent design decisions.

Lesson 2.1: Traffic counting and peak-hour factor

Physical and planning meaning

Permanent counters, short-duration manual counts and image detectors have different bias mechanisms. Peak-hour factor compares one-hour volume to the flow implied by its busiest subinterval. It captures peaking but does not correct detection errors or define demand suppressed by queue spillback.

Governing relationship

PHF = V_hour/(4V_15,max).

Interpreted worked case

Four consecutive 15-minute counts of 200, 240, 310 and 250 vehicles give a one-hour volume of 1,000. The peak-hour factor is 1000/(4×310) = 0.806. A PHF below one indicates an uneven distribution of volume across quarters.

Investigating an assumption

Explain why a congested detector may show declining throughput as conditions worsen even while arrival demand increases.

Important limitations

Count direction, lane, vehicle classification and weather. Never combine nonconcurrent 15-minute counts and label them a single observed hour.

Sensitivity and uncertainty

Vary one uncertain parameter while keeping others fixed, then vary a second parameter jointly. A steep response indicates that the result should be reported as a range rather than a single number. Do not equate a sensitivity exercise with a statistical confidence interval; uncertainty also includes model structure and measurement bias.

Three distinct evidence-based investigations

Investigation 2.1.1

An agency is reviewing an industrial connection used by large goods vehicles and irregular delivery convoys. The primary affected users include truck drivers and nearby residents. Before transferring the relationship PHF = V_hour/(4V_15,max). to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for reliable travel time, pavement damage and turning geometry; compare that baseline with an alternative centred on a freight-access management plan. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.1.2

Use this worked reference as a challenge rather than as a template to memorize: Four consecutive 15-minute counts of 200, 240, 310 and 250 vehicles give a one-hour volume of 1,000. The peak-hour factor is 1000/(4×310) = 0.806. A PHF below one indicates an uneven distribution of volume across quarters. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.1.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Count direction, lane, vehicle classification and weather. Never combine nonconcurrent 15-minute counts and label them a single observed hour. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of traffic counting and peak-hour factor. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (PHF = V_hour/(4V_15,max).), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about traffic counting and peak-hour factor, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.2: Travel time, speed surveys and reliability

Physical and planning meaning

A spot-speed survey observes passing vehicles, whereas corridor travel-time studies follow trips between endpoints. Travel-time reliability concerns variation between days and periods, not only the mean. Delay definitions depend on a selected reference time and can change simply because the benchmark changes.

Governing relationship

T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined.

Interpreted worked case

An 8 km corridor takes 12 minutes on a typical day but 21 minutes on the 95th-percentile day. With a 10-minute free-flow benchmark, the planning time index is 2.1; this is not a claim that ninety-five percent of drivers travel exactly 21 minutes.

Investigating an assumption

Which percentile is relevant to emergency vehicles compared with a commuter who can vary departure time?

Important limitations

Small travel-time samples and GPS traces with poor map matching can create false precision in high percentiles.

Measurement protocol

Specify which observations distinguish the proposed engineering relationship from a coincidental correlation. Record station location, observation interval, vehicles or users included, calibration checks and missing data. A correct equation evaluated on a biased dataset can create a less defensible conclusion than a rough estimate accompanied by an explicit uncertainty interval.

Three distinct evidence-based investigations

Investigation 2.2.1

An agency is reviewing a busy intersection surrounded by stores, loading areas and narrow footpaths. The primary affected users include local businesses, cyclists and walkers. Before transferring the relationship T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for conflicting curb use and vehicle queues; compare that baseline with an alternative centred on reassignment of curb space with inclusive access. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.2.2

Use this worked reference as a challenge rather than as a template to memorize: An 8 km corridor takes 12 minutes on a typical day but 21 minutes on the 95th-percentile day. With a 10-minute free-flow benchmark, the planning time index is 2.1; this is not a claim that ninety-five percent of drivers travel exactly 21 minutes. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.2.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Small travel-time samples and GPS traces with poor map matching can create false precision in high percentiles. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of travel time, speed surveys and reliability. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about travel time, speed surveys and reliability, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.3: Capacity, bottlenecks and level of service

Physical and planning meaning

Demand, throughput and capacity are distinct quantities. A bottleneck may discharge fewer vehicles than arrive, building a queue. Lane-group saturation flow and multilane facility capacity depend on geometry, traffic composition, roadside friction and control. Level-of-service letter grades are framework-dependent and should not be equated with a universal measure of road success.

Governing relationship

v/c = demand/capacity for matched units and time periods.

Interpreted worked case

For demand of 1,900 veh/h and measured throughput capacity of 1,600 veh/h, v/c = 1.1875. The excess arrival rate is approximately 300 veh/h during sustained oversaturation, provided demand and service remain roughly constant.

Investigating an assumption

What observations would tell you whether the corridor bottleneck is geometric, signal-controlled, incident-related or caused by a downstream queue?

Important limitations

Capacity is not a single transferable constant. Use a location- and jurisdiction-appropriate method with calibrated inputs.

Dimensional audit

Before computing, rewrite every quantity in one coherent system of units. A time rate needs a time denominator; a spatial density needs a road-length denominator; a percentage must become a decimal before entering an algebraic equation. Unit checking can reject many plausible-looking but incorrect answers without any specialist software.

Three distinct evidence-based investigations

Investigation 2.3.1

An agency is reviewing a developing area with uncertain future traffic and pedestrian demand. The primary affected users include new residents and existing communities. Before transferring the relationship v/c = demand/capacity for matched units and time periods. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for forecast error and long-lived capital decisions; compare that baseline with an alternative centred on phased investment with monitoring triggers. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.3.2

Use this worked reference as a challenge rather than as a template to memorize: For demand of 1,900 veh/h and measured throughput capacity of 1,600 veh/h, v/c = 1.1875. The excess arrival rate is approximately 300 veh/h during sustained oversaturation, provided demand and service remain roughly constant. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.3.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Capacity is not a single transferable constant. Use a location- and jurisdiction-appropriate method with calibrated inputs. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of capacity, bottlenecks and level of service. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (v/c = demand/capacity for matched units and time periods.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about capacity, bottlenecks and level of service, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.4: Origin–destination studies and sampling

Physical and planning meaning

Transport planning needs to understand who is traveling, from where, to where, when and by what mode. Roadside interviews, household surveys, smart cards, ticketing systems, mobile data and plate matching each observe only subsets of the travel market. Sampling weights can adjust measured groups, but cannot automatically correct missing or systematically excluded populations.

Governing relationship

Expanded trips in group i = observed sampled trips / sampling fraction_i.

Interpreted worked case

If 30 sampled trips represent 10% of an eligible population, an expanded estimate is 300 trips for that specific sampling frame. Applying the same multiplier to every district is invalid when sampling rates and nonresponse differ.

Investigating an assumption

Construct a minimal survey that can distinguish trips by purpose and mode without collecting unnecessary identifying personal information.

Important limitations

Privacy, consent, aggregation and unequal phone or internet access limit data use even when technical linkage is possible.

Alternative design comparison

Compare at least two interventions against the same baseline. Record whether each changes demand, available capacity, exposure, pavement condition, human behavior or monitoring quality. An alternative with the largest improvement in one performance indicator is not necessarily superior when its cost, emissions, access impacts and failure modes are considered.

Three distinct evidence-based investigations

Investigation 2.4.1

An agency is reviewing a two-lane route linking distant towns with limited passing opportunities. The primary affected users include intercity travelers and bus passengers. Before transferring the relationship Expanded trips in group i = observed sampled trips / sampling fraction_i. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for severe-crash exposure and overtaking constraints; compare that baseline with an alternative centred on targeted safety improvements rather than indiscriminate widening. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.4.2

Use this worked reference as a challenge rather than as a template to memorize: If 30 sampled trips represent 10% of an eligible population, an expanded estimate is 300 trips for that specific sampling frame. Applying the same multiplier to every district is invalid when sampling rates and nonresponse differ. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.4.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Privacy, consent, aggregation and unequal phone or internet access limit data use even when technical linkage is possible. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of origin–destination studies and sampling. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (Expanded trips in group i = observed sampled trips / sampling fraction_i.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about origin–destination studies and sampling, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Unit synthesis exercise

Connect traffic counting and peak-hour factor with origin–destination studies and sampling: imagine a corridor proposal that improves the first measure but worsens the last. Write a one-page recommendation showing the trade-off, a minimum dataset for testing both effects, and the circumstances that would reverse your recommendation.

Reading the laboratory outputs

Every chart is calculated for hypothetical parameters. Change one parameter at a time to understand the model. Check a point by hand. Export a CSV, note the axes, then identify at least one physically relevant effect omitted by the model. No laboratory in this book is approved for design or real-time operational control.

Extended field case: a bottleneck misidentified from detector data

Imagine a two-kilometre approach to a bridge where heavy vehicles merge into one lane. An upstream counter records 1,700 vehicles entering during a measured hour. A second counter at the bridge records 1,420 vehicles departing. If there are no intervening exits and the observation clocks are synchronized, the difference of 280 vehicles suggests accumulation within the segment. It does not mean every new vehicle experiences the same delay. A queue may block an upstream roundabout, changing arrivals, while the measured downstream throughput may temporarily rise or fall depending on how a platoon reaches the merge.

The first data-quality check is conservation of vehicles. Count the number already within the segment at the beginning and end of the period. Identify any intermediate driveways, bus stops or ramps. With an initial inventory of 50 vehicles and no other entry or exit, the inventory at the end would be 330, assuming exact counts and no missing movements. If a parking access admits 90 additional vehicles, the accounting must include them. An apparent unexplained buildup could instead be a counting mismatch or detector clock offset. Concluding that a bridge needs additional lanes before reconciling this accounting is a potentially expensive error.

A capacity study uses the appropriate time interval. A queue accumulated over an hour can be served later when demand drops. To estimate a 30-minute buildup from stable rates, calculate 0.5 × (1,700−1,420) = 140 vehicles. This is a deterministic expectation, not the precise count of cars in a traffic jam. Vehicle spacing must be measured or estimated to translate queue count into physical length, and that spacing depends on heavy vehicles and whether the queue is stationary or creeping. If a queue takes an urban junction's limited storage, congestion may spread beyond the segment and invalidate the assumed 1,700 vehicles/h entry rate.

A second study should record point travel times through the bottleneck. A corridor can have modest throughput with acceptable travel time when demand is low; during overload, identical throughput may occur with a much longer queue. Gather floating-car or anonymized trajectory times, synchronized detector volumes, heavy-vehicle proportions and obstruction reports. Use a scatter plot of travel time against entering demand, but do not claim a causal relationship without checking time-of-day and incidents. Travel-time reliability also matters: a 20-minute average with frequent 60-minute extremes may be unacceptable for emergency services.

The decision memo should compare a merge-control treatment, restrictions on stopping near the bridge, improved incident management and geometric expansion. Report approximate capital cost, expected construction disruption, accessibility effects and the evidence threshold needed to proceed. A large investment is not automatically justified by a single high v/c ratio. Repeated, high-quality field observations and a corridor-wide evaluation could reveal lower-cost interventions that remove the operational bottleneck without adding a lane.

Unit 2: Traffic Data and Roadway Performance

Book 14 · Transportation Engineering · Civil Engineering

Learning objectives

Learn to characterize the mechanisms, assumptions and observational evidence connecting traffic counting and peak-hour factor, travel time, speed surveys and reliability, capacity, bottlenecks and level of service, origin–destination studies and sampling. Demonstrate unit consistency, interpret safety and access alongside vehicle throughput, and compare model predictions against field observations before making recommendations.

How to study this unit

Work through all four lessons, reproduce each numerical illustration without copying the result, operate the linked investigations, then answer the unit assessment and prepare a short project memo. Recognize which assumptions are useful for learning and which prevent design decisions.

Lesson 2.1: Traffic counting and peak-hour factor

Physical and planning meaning

Permanent counters, short-duration manual counts and image detectors have different bias mechanisms. Peak-hour factor compares one-hour volume to the flow implied by its busiest subinterval. It captures peaking but does not correct detection errors or define demand suppressed by queue spillback.

Governing relationship

PHF = V_hour/(4V_15,max).

Interpreted worked case

Four consecutive 15-minute counts of 200, 240, 310 and 250 vehicles give a one-hour volume of 1,000. The peak-hour factor is 1000/(4×310) = 0.806. A PHF below one indicates an uneven distribution of volume across quarters.

Investigating an assumption

Explain why a congested detector may show declining throughput as conditions worsen even while arrival demand increases.

Important limitations

Count direction, lane, vehicle classification and weather. Never combine nonconcurrent 15-minute counts and label them a single observed hour.

Sensitivity and uncertainty

Vary one uncertain parameter while keeping others fixed, then vary a second parameter jointly. A steep response indicates that the result should be reported as a range rather than a single number. Do not equate a sensitivity exercise with a statistical confidence interval; uncertainty also includes model structure and measurement bias.

Three distinct evidence-based investigations

Investigation 2.1.1

An agency is reviewing an industrial connection used by large goods vehicles and irregular delivery convoys. The primary affected users include truck drivers and nearby residents. Before transferring the relationship PHF = V_hour/(4V_15,max). to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for reliable travel time, pavement damage and turning geometry; compare that baseline with an alternative centred on a freight-access management plan. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.1.2

Use this worked reference as a challenge rather than as a template to memorize: Four consecutive 15-minute counts of 200, 240, 310 and 250 vehicles give a one-hour volume of 1,000. The peak-hour factor is 1000/(4×310) = 0.806. A PHF below one indicates an uneven distribution of volume across quarters. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.1.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Count direction, lane, vehicle classification and weather. Never combine nonconcurrent 15-minute counts and label them a single observed hour. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of traffic counting and peak-hour factor. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (PHF = V_hour/(4V_15,max).), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about traffic counting and peak-hour factor, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.2: Travel time, speed surveys and reliability

Physical and planning meaning

A spot-speed survey observes passing vehicles, whereas corridor travel-time studies follow trips between endpoints. Travel-time reliability concerns variation between days and periods, not only the mean. Delay definitions depend on a selected reference time and can change simply because the benchmark changes.

Governing relationship

T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined.

Interpreted worked case

An 8 km corridor takes 12 minutes on a typical day but 21 minutes on the 95th-percentile day. With a 10-minute free-flow benchmark, the planning time index is 2.1; this is not a claim that ninety-five percent of drivers travel exactly 21 minutes.

Investigating an assumption

Which percentile is relevant to emergency vehicles compared with a commuter who can vary departure time?

Important limitations

Small travel-time samples and GPS traces with poor map matching can create false precision in high percentiles.

Measurement protocol

Specify which observations distinguish the proposed engineering relationship from a coincidental correlation. Record station location, observation interval, vehicles or users included, calibration checks and missing data. A correct equation evaluated on a biased dataset can create a less defensible conclusion than a rough estimate accompanied by an explicit uncertainty interval.

Three distinct evidence-based investigations

Investigation 2.2.1

An agency is reviewing a busy intersection surrounded by stores, loading areas and narrow footpaths. The primary affected users include local businesses, cyclists and walkers. Before transferring the relationship T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for conflicting curb use and vehicle queues; compare that baseline with an alternative centred on reassignment of curb space with inclusive access. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.2.2

Use this worked reference as a challenge rather than as a template to memorize: An 8 km corridor takes 12 minutes on a typical day but 21 minutes on the 95th-percentile day. With a 10-minute free-flow benchmark, the planning time index is 2.1; this is not a claim that ninety-five percent of drivers travel exactly 21 minutes. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.2.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Small travel-time samples and GPS traces with poor map matching can create false precision in high percentiles. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of travel time, speed surveys and reliability. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (T = L/v, and planning time index = T_95/T_free when those travel times are consistently defined.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about travel time, speed surveys and reliability, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.3: Capacity, bottlenecks and level of service

Physical and planning meaning

Demand, throughput and capacity are distinct quantities. A bottleneck may discharge fewer vehicles than arrive, building a queue. Lane-group saturation flow and multilane facility capacity depend on geometry, traffic composition, roadside friction and control. Level-of-service letter grades are framework-dependent and should not be equated with a universal measure of road success.

Governing relationship

v/c = demand/capacity for matched units and time periods.

Interpreted worked case

For demand of 1,900 veh/h and measured throughput capacity of 1,600 veh/h, v/c = 1.1875. The excess arrival rate is approximately 300 veh/h during sustained oversaturation, provided demand and service remain roughly constant.

Investigating an assumption

What observations would tell you whether the corridor bottleneck is geometric, signal-controlled, incident-related or caused by a downstream queue?

Important limitations

Capacity is not a single transferable constant. Use a location- and jurisdiction-appropriate method with calibrated inputs.

Dimensional audit

Before computing, rewrite every quantity in one coherent system of units. A time rate needs a time denominator; a spatial density needs a road-length denominator; a percentage must become a decimal before entering an algebraic equation. Unit checking can reject many plausible-looking but incorrect answers without any specialist software.

Three distinct evidence-based investigations

Investigation 2.3.1

An agency is reviewing a developing area with uncertain future traffic and pedestrian demand. The primary affected users include new residents and existing communities. Before transferring the relationship v/c = demand/capacity for matched units and time periods. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for forecast error and long-lived capital decisions; compare that baseline with an alternative centred on phased investment with monitoring triggers. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.3.2

Use this worked reference as a challenge rather than as a template to memorize: For demand of 1,900 veh/h and measured throughput capacity of 1,600 veh/h, v/c = 1.1875. The excess arrival rate is approximately 300 veh/h during sustained oversaturation, provided demand and service remain roughly constant. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.3.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Capacity is not a single transferable constant. Use a location- and jurisdiction-appropriate method with calibrated inputs. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of capacity, bottlenecks and level of service. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (v/c = demand/capacity for matched units and time periods.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about capacity, bottlenecks and level of service, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Lesson 2.4: Origin–destination studies and sampling

Physical and planning meaning

Transport planning needs to understand who is traveling, from where, to where, when and by what mode. Roadside interviews, household surveys, smart cards, ticketing systems, mobile data and plate matching each observe only subsets of the travel market. Sampling weights can adjust measured groups, but cannot automatically correct missing or systematically excluded populations.

Governing relationship

Expanded trips in group i = observed sampled trips / sampling fraction_i.

Interpreted worked case

If 30 sampled trips represent 10% of an eligible population, an expanded estimate is 300 trips for that specific sampling frame. Applying the same multiplier to every district is invalid when sampling rates and nonresponse differ.

Investigating an assumption

Construct a minimal survey that can distinguish trips by purpose and mode without collecting unnecessary identifying personal information.

Important limitations

Privacy, consent, aggregation and unequal phone or internet access limit data use even when technical linkage is possible.

Alternative design comparison

Compare at least two interventions against the same baseline. Record whether each changes demand, available capacity, exposure, pavement condition, human behavior or monitoring quality. An alternative with the largest improvement in one performance indicator is not necessarily superior when its cost, emissions, access impacts and failure modes are considered.

Three distinct evidence-based investigations

Investigation 2.4.1

An agency is reviewing a two-lane route linking distant towns with limited passing opportunities. The primary affected users include intercity travelers and bus passengers. Before transferring the relationship Expanded trips in group i = observed sampled trips / sampling fraction_i. to the location, explain which inputs could be directly observed, which would need a proxy, and which are not meaningful without a local policy or design standard. Establish a baseline for severe-crash exposure and overtaking constraints; compare that baseline with an alternative centred on targeted safety improvements rather than indiscriminate widening. Your written answer must separate improvements that follow from a formula from improvements that are only plausible hypotheses. Address conditions in which the intervention would fail.

Investigation 2.4.2

Use this worked reference as a challenge rather than as a template to memorize: If 30 sampled trips represent 10% of an eligible population, an expanded estimate is 300 trips for that specific sampling frame. Applying the same multiplier to every district is invalid when sampling rates and nonresponse differ. Replace one input with a plausible field observation, state why the replacement is plausible, and predict the direction of the result before computing. If the prediction differs from the calculation, investigate units and the sign convention first. Explain how uncertainty from measurement, behavior and a simplified model affects the credibility of a precise numerical answer.

Investigation 2.4.3

A project manager proposes to use a single number from this topic to approve an investment. Prepare a critique rooted in the following limitation: Privacy, consent, aggregation and unequal phone or internet access limit data use even when technical linkage is possible. List two measurements that would improve the study, one externality that the calculation does not represent and one practical decision that could be deferred until the missing evidence is obtained. Avoid claiming the educational model has been professionally calibrated or validated on a real transportation network.

Explain the computation to a classmate

Without looking at the numerical illustration, reconstruct the logic of origin–destination studies and sampling. Start with the field measurement and unit definition, define the reference condition, state the idealization in the equation (Expanded trips in group i = observed sampled trips / sampling fraction_i.), and then walk through the calculation so that a reader can independently check every value. Identify which result can be checked by a second technique. If observations contradict the simple model, explain how you would revise the model rather than discard the observations.

Field data and reporting protocol

For a short field exercise about origin–destination studies and sampling, specify a time window, observation location, data dictionary and recording method. Protect individual privacy by avoiding unnecessary vehicle or personal identifiers. Use at least three quality checks: plausibility ranges for the raw input, missing-value counts, and a reconciliation of any aggregated totals. Present separate notes on systematic bias, day-to-day variability and uncertainty in the equation itself.

Decision boundary

The relationship in this lesson is appropriate for concept exploration and transparent back-of-the-envelope screening. A code-compliant operational decision would additionally need jurisdiction-specific requirements, evidence of relevant site conditions and a documented professional review. Name the exact assumptions that would need replacement and identify stakeholders who might be excluded if the decision were made entirely from a vehicle-centred metric.

Unit synthesis exercise

Connect traffic counting and peak-hour factor with origin–destination studies and sampling: imagine a corridor proposal that improves the first measure but worsens the last. Write a one-page recommendation showing the trade-off, a minimum dataset for testing both effects, and the circumstances that would reverse your recommendation.

Reading the laboratory outputs

Every chart is calculated for hypothetical parameters. Change one parameter at a time to understand the model. Check a point by hand. Export a CSV, note the axes, then identify at least one physically relevant effect omitted by the model. No laboratory in this book is approved for design or real-time operational control.

Extended field case: a bottleneck misidentified from detector data

Imagine a two-kilometre approach to a bridge where heavy vehicles merge into one lane. An upstream counter records 1,700 vehicles entering during a measured hour. A second counter at the bridge records 1,420 vehicles departing. If there are no intervening exits and the observation clocks are synchronized, the difference of 280 vehicles suggests accumulation within the segment. It does not mean every new vehicle experiences the same delay. A queue may block an upstream roundabout, changing arrivals, while the measured downstream throughput may temporarily rise or fall depending on how a platoon reaches the merge.

The first data-quality check is conservation of vehicles. Count the number already within the segment at the beginning and end of the period. Identify any intermediate driveways, bus stops or ramps. With an initial inventory of 50 vehicles and no other entry or exit, the inventory at the end would be 330, assuming exact counts and no missing movements. If a parking access admits 90 additional vehicles, the accounting must include them. An apparent unexplained buildup could instead be a counting mismatch or detector clock offset. Concluding that a bridge needs additional lanes before reconciling this accounting is a potentially expensive error.

A capacity study uses the appropriate time interval. A queue accumulated over an hour can be served later when demand drops. To estimate a 30-minute buildup from stable rates, calculate 0.5 × (1,700−1,420) = 140 vehicles. This is a deterministic expectation, not the precise count of cars in a traffic jam. Vehicle spacing must be measured or estimated to translate queue count into physical length, and that spacing depends on heavy vehicles and whether the queue is stationary or creeping. If a queue takes an urban junction's limited storage, congestion may spread beyond the segment and invalidate the assumed 1,700 vehicles/h entry rate.

A second study should record point travel times through the bottleneck. A corridor can have modest throughput with acceptable travel time when demand is low; during overload, identical throughput may occur with a much longer queue. Gather floating-car or anonymized trajectory times, synchronized detector volumes, heavy-vehicle proportions and obstruction reports. Use a scatter plot of travel time against entering demand, but do not claim a causal relationship without checking time-of-day and incidents. Travel-time reliability also matters: a 20-minute average with frequent 60-minute extremes may be unacceptable for emergency services.

The decision memo should compare a merge-control treatment, restrictions on stopping near the bridge, improved incident management and geometric expansion. Report approximate capital cost, expected construction disruption, accessibility effects and the evidence threshold needed to proceed. A large investment is not automatically justified by a single high v/c ratio. Repeated, high-quality field observations and a corridor-wide evaluation could reveal lower-cost interventions that remove the operational bottleneck without adding a lane.