Highway Assignment

Validation of final highway assignment was done comparing model outputs from the final loaded networks with observed data. The comparisons were done with volumes and speeds.

Volumes

The validation results for the Highway Assignment portion of the model are shown in this section. The observed data for 2019 volumes is taken from the Utah Department of Transportation (UDOT) Average Annual Daily Traffic (AADT) History and associated with their respective model segments. The traffic model data is taken from segment summary report for the 2019 base year model: v9_SE19_Net19_Summary_SEGID.csv. The results are divided into three sections:

• Summary Comparison
• Detailed Comparison
• Map Comparison

Code

import {GroupedBarChart} from "@d3/grouped-bar-chart"
import {Legend, Swatches} from "@d3/color-legend"
import {howto, altplot} from "@d3/example-components"

Summary Comparison

The summary comparison shows region and county-wide differences between model and observed for Average Daily Volume and Vehicle-Miles Traveled (VMT) by vehicle type. The values for Box Elder and Weber counties are only the portions within the MPO planning area. Validation was checked comparing the average daily volume at the region and county levels. Figure 1, below, contains an interactive view of model vs observed differences by roadway class and vehicle type.

Code

html`<br/>`

Code

viewof bSummaryFuncClass = Inputs.select(new Map([['All Roadways','All Roadways'], ['Freeway','Freeway'], ['Principal','Principal'], ['Minor', 'Minor'], ['Collector', 'Collector']]), {value: 'All Roadways', label: "Roadway Class:"})
viewof bSummaryVehType = Inputs.select(new Map([['All Vehicles','All Vehicles'], ['Passenger Cars', 'Passenger Cars'], ['Medium Trucks','Medium Trucks'], ['Heavy Trucks','Heavy Trucks']]), {value: 'All Vehicles', label: "Vehicle Type:"})
viewof bSummaryDiffType = Inputs.select(new Map([['Percent Difference','DiffPct'], ['Difference','Diff']]), {value: 'DiffPct', label: "Display:"})

Code

volDiffLongT = transpose(volDiffLong)
vmtDiffLongT = transpose(vmtDiffLong)

volDiffLongT_filtered = volDiffLongT.filter(function(dataL) {
    return bSummaryFuncClass == dataL.funcClass &&
           bSummaryVehType == dataL.vehType &&
           (('vol' + bSummaryDiffType) == dataL.View);
})
vmtDiffLongT_filtered = vmtDiffLongT.filter(function(dataL) {
    return bSummaryFuncClass == dataL.funcClass &&
           bSummaryVehType == dataL.vehType &&
           (('vmt' + bSummaryDiffType) == dataL.View);
})

vvp = transpose(vvpct)
vvpL = transpose(vvpctLong)
vvaL = transpose(vvabsLong)
vvaLR = transpose(vvabsLongR)

Code

import {DivergingBarChart} from "@d3/diverging-bar-chart"

function getXDomainVol(bSummaryDiffType) {
    if (bSummaryDiffType === "Diff") {
        return [max_abs_value_volDiff * -1, max_abs_value_volDiff];
    } else {
        return [max_abs_value_volDiffPct * -1, max_abs_value_volDiff]; // -100% to 100%
    }
}

function getXDomainVmt(bSummaryDiffType) {
    if (bSummaryDiffType === "Diff") {
        return [max_abs_value_vmtDiff * -1, max_abs_value_vmtDiff];
    } else {
        return [max_abs_value_vmtDiffPct * -1, max_abs_value_vmtDiff]; // -100% to 100%
    }
}

Code

html`<br/><h4>Average Daily Volume</h4>`

Code

chartVolDiff = DivergingBarChart(volDiffLongT_filtered, {
    x: d => d.ViewValue,
    y: d => d.coFips,
    xFormat: bSummaryDiffType === "Diff" ? "+,d" : "+.1%",
    xLabel: "Model vs Observed Differences",
    width: 440,
    xDomain: bSummaryDiffType === "Diff" ? [max_abs_value_volDiff * -1, max_abs_value_volDiff] : [max_abs_value_volDiffPct * -1, max_abs_value_volDiffPct],
    yDomain:  ['Region','Box Elder County - WFRC','Weber County - WFRC','Davis County','Salt Lake County','Utah County'],
    colors: d3.schemeRdBu[3]
})

Code

html`<br/><h4>Vehicle-Miles Traveled</h4>`

Code

chartVmtDiff = DivergingBarChart(vmtDiffLongT_filtered, {
    x: d => d.ViewValue,
    y: d => d.coFips,
    xFormat: bSummaryDiffType === "Diff" ? "+,d" : "+.1%",
    xLabel: "Model vs Observed Differences",
    width: 440,
    xDomain: bSummaryDiffType === "Diff" ? [max_abs_value_vmtDiff * -1, max_abs_value_vmtDiff] : [max_abs_value_vmtDiffPct * -1, max_abs_value_vmtDiffPct],
    yDomain:  ['Region','Box Elder County - WFRC','Weber County - WFRC','Davis County','Salt Lake County','Utah County'],
    colors: d3.schemeRdBu[3]
})

Code

tbEmptyCell1 = 1

Figure 1: Highway Assignment Summary Comparison

At the region level model volume is 0.4% lower than observed volume. The four more urban counties (Weber, Davis, Salt Lake, and Davis) were all within 5% of observed volumes with Salt Lake County being the closest. Weber and Davis were slightly lower and Utah County was slightly higher. Box Elder County is more rural than the other counties. Box Elder model volumes are about 10% lower than observed. Time did not allow for further calibration of the volumes in Box Elder area to account for the larger differences.

One important observation at the Collector and All Vehicles level is that Utah County shows a much higher difference than the other counties. Upon further investigation of observed Collector volumes in Utah County, many roadway segments had very low volumes compared to what was expected. Utah County is one of the highest growth areas in the region. For this reason, we expect that the observed count data may be underrepresenting actual volumes. We also anticipate observed volumes in Utah County to improve in the near-term. Within the last several years, a large investment in continuous count station in Utah County has been made. The new counters will add additional information to generate observed volumes for all roadway segments.

The largest differences in model vs observed volumes occur in the Medium Truck and Heavy Truck vehicle types. A good amount of time was spent attempting to bring model truck volumes closer to observed. However, due to the limited data sources for truck information, further need to investigate observed truck volumes, and a desire to not over-calibrate the model, further calibration was stopped. Truck modeling remains a future priority for model improvement.

Detailed Comparison

The model vs observed details in this section are presented by volume and Vehicle-Miles Traveled (VMT) through the comparison of model and observed data facility type by region and also by county. Figure 2 allows for the interactive visual comparison of model and observed values for the region and each county for all vehicles, cars, medium trucks, and heavy trucks. The comparisons are shown in four different types of charts and tables:

• Average Daily Volume by Roadway Class (2a): The daily volume is averaged across all segments within their respective geography and vehicle type.
• Total VMT by Roadway Class (2b): For each segment*, the daily volume is multiplied by segment distance and then summed across all segments within their respective geography and vehicle type.
• Model vs Count Segment Volume (2c): This is a scatter plot of segment daily volume with the x-axis as the observed volume and the y-axis as the model volume. The red line shows the location of where model and observed volumes are equal. The dashed blue line shows a least-squares linear regression. The further the blue line moved away from the red line, the further the model is from observed.
• Segment Percent Error (2d): This is a scatter plot showing the amount of error (percent difference) between the observed volume and the model volume. The observed volume is the x-axis and the percent error is the y-axis. The red lines are a bounding box that shows the control target. As volume increases, it is expected that the percent error should decrease.

Code

html`<br/>`

Code

viewof bCountySelect = Inputs.select(new Map([['Region', 'Region'], ['Box Elder County - WFRC',3], ['Weber County - WFRC',57], ['Davis County',11], ['Salt Lake County',35], ['Utah County',49]]), {value: 'All', label: "Geography:"})
viewof bVehType = Inputs.select(new Map([['All Vehicles','All Vehicles'], ['Passenger Cars', 'Passenger Cars'], ['Medium Trucks','Medium Trucks'], ['Heavy Trucks','Heavy Trucks']]), {value: 'All Vehicles', label: "Vehicle Type:"})

sortOrder = ['Freeway', 'Principal', 'Minor', 'Collector', 'All Roadways'];

volT = transpose(vol)
vmtT = transpose(vmt)
filtered_volData = volT.filter(function(dataL) {
    return bCountySelect == dataL.coFips &&
           bVehType == dataL.vehType;
}).sort((a, b) => sortOrder.indexOf(a.funcClass) - sortOrder.indexOf(b.funcClass));
filtered_vmtData = vmtT.filter(function(dataL){
    return bCountySelect == dataL.coFips &&
           bVehType == dataL.vehType;
}).sort((a, b) => sortOrder.indexOf(a.funcClass) - sortOrder.indexOf(b.funcClass));

volTL = transpose(volLong)
vmtTL = transpose(vmtLong)
filtered_volDataL = volTL.filter(function(dataL) {
    return bCountySelect == dataL.coFips &&
           bVehType == dataL.vehType;
}).sort((a, b) => sortOrder.indexOf(a.funcClass) - sortOrder.indexOf(b.funcClass));
filtered_vmtDataL = vmtTL.filter(function(dataL){
    return bCountySelect == dataL.coFips &&
           bVehType == dataL.vehType;
}).sort((a, b) => sortOrder.indexOf(a.funcClass) - sortOrder.indexOf(b.funcClass));


allvehplotT = transpose(allvehplot)
filtered_allvehplotData = allvehplotT.filter(function(dataL) {
    return bCountySelect == dataL.coFips &&
           bVehType == dataL.vehType;
});

Code

function formatNumber(value, isPercentage=false) {
    if (typeof value === 'undefined') {
        return '';  // or return a default value or message
    }
    
    if (isPercentage) {
        return (Number(value) * 100).toFixed(1) + '%';
    }
    return Number(value.toFixed(0)).toLocaleString();
}

widthsVol = ['100px', '52px', '70px', '70px', '73px', '73px', '63px', '63px']; // Define the widths
widthsVmt = ['100px', '88px', '88px', '88px', '88px'];

Code

html`
<h4>2a. Average Daily Volume by Roadway Class</h4>
<table>
    <thead>
    <tr>
        ${["Roadway Class", "# Segs", "Volume", "Observed", "Difference", "Percent Difference", "RMSE", "Percent RMSE"].map((d, i) => {
            return html`<th style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; width: ${widthsVol[i]};'>${d}</th>`;
        })}
    </tr>
    </thead>
    <tbody>
        ${filtered_volData.map(row => {
            const isBold = row['funcClass'] === 'All Roadways';
            return html`<tr style='border-bottom: 1px solid lightgrey;'>
                ${["funcClass", "numSegs", "volMod", "volObs", "volDiff", "volDiffPct", "volRmse", "volRmsePct"].map((d, i) => {
                    // Check if the current cell is one of the numeric columns that need formatting
                    let formattedValue;
                    if (i === 5 || i === 7) {
                        formattedValue = formatNumber(row[d], true);  // True for percentage formatting
                    } else if ((i >= 1 && i <= 4) || i==6) {
                        formattedValue = formatNumber(row[d]);
                    } else {
                        formattedValue = row[d];
                    }
                    return html`<td style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; font-weight: ${isBold ? 'bold' : 'normal'};'>${formattedValue}</td>`;
                })}
            </tr>`;
        })}
    </tbody>
</table>`

Code

html`<h4>&nbsp;</h4>`

Code

keyVol = Legend(bChartVol.scales.color, {title: "Data Source"})

bChartVol = GroupedBarChart(filtered_volDataL, {
    x: d => d.funcClass,
    y: d => d.ViewValue,
    z: d => d.DataSource,
    xDomain: ['Freeway','Principal','Minor','Collector','All Roadways'],
    yLabel: "Volume (thousands)",
    zDomain: ['Model','Observed'],
    width: 320,
    height: 175,
    colors: ["#376092", "#77933c"]
})

Code

html`
<h4>2b. Total Daily VMT by Roadway Class</h4>
<table>
    <thead>
    <tr>
        ${["Roadway Class", "Model", "Observed", "Difference", "Percent Difference"].map((d, i) => {
            return html`<th style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; width: ${widthsVmt[i]};'>${d}</th>`;
        })}
    </tr>
    </thead>
    <tbody>
        ${filtered_vmtData.map(row => {
            const isBold = row['funcClass'] === 'All Roadways';
            return html`<tr style='border-bottom: 1px solid lightgrey;'>
                ${["funcClass", "vmtMod", "vmtObs", "vmtDiff", "vmtDiffPct"].map((d, i) => {
                    // Check if the current cell is one of the numeric columns that need formatting
                    let formattedValue;
                    if (i === 4 || i === 6) {
                        formattedValue = formatNumber(row[d], true);  // True for percentage formatting
                    } else if ((i >= 1 && i <= 3) || i==5) {
                        formattedValue = formatNumber(row[d]);
                    } else {
                        formattedValue = row[d];
                    }
                    return html`<td style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; font-weight: ${isBold ? 'bold' : 'normal'};'>${formattedValue}</td>`;
                })}
            </tr>`;
        })}
    </tbody>
</table>`

Code

html`<h4>&nbsp;</h4>`

Code

keyVmt = Legend(bChartVmt.scales.color, {title: "Data Source"})

bChartVmt = GroupedBarChart(filtered_vmtDataL, {
    x: d => d.funcClass,
    y: d => d.ViewValue,
    z: d => d.DataSource,
    xDomain: ['Freeway','Principal','Minor','Collector','All Roadways'],
    yLabel: "VMT (millions)",
    zDomain: ['Model','Observed'],
    width: 320,
    height: 175,
    colors: ["#376092", "#77933c"]
})

Code

import {max} from 'd3-array';

Code

maxVal = {
  return Math.max(
    d3.max(filtered_allvehplotData, d => d.volObs),
    d3.max(filtered_allvehplotData, d => d.volMod)
  );
}

Code

html`<h4>2c. Model vs Observed Volumes</h4>`

Code

Plot.plot({
  grid: true,
  width: 460,
  height: 300,
  marginRight: 40,
  x: {
    label: "Observed Volume (thousands)",
    domain: [0, maxVal]
  },
  y: {
    label: "Model Volume (thousands)",
    domain: [0, maxVal]
  },
  marks: [
    Plot.dot(filtered_allvehplotData, {
      x: "volObs",
      y: "volMod",
      r: 1,
      fill: "rgb(80, 116, 230)",
      fillOpacity: 0.5,
      stroke: "none"
    }),
    Plot.link([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x1: 0,
      y1: 0,
      x2: maxVal,
      y2: (k) => maxVal * k,
      strokeOpacity: (k) => k === 1 ? 1 : 0.2,
      stroke: "red",
      strokeWidth: (k) => k === 1 ? 2 : 1.5
    }),
    Plot.text([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x: maxVal,
      y: (k) => maxVal * k,
      text: ((f) => (k) => k === 1 ? "Equal" : f(k - 1))(d3.format("+.0%")),
      textAnchor: "start",
      dx: 6
    }),
    Plot.linearRegressionY(filtered_allvehplotData, {
        x: "volObs",
        y: "volMod",
        stroke: "rgb(80, 116, 230)",
        strokeDasharray: "4 4",  // This creates a dashed line pattern,
        strokeWidth: 2 
    })
  ]
})

Code

html`<h4>2d. Segment Percent Error</h4>`

Code

Plot.plot({
  grid: true,
  width: 460,
  height: 300,
  marginRight: 40,
  x: {
    label: "Observed Volume (thousands)",
    domain: [0, maxVal]
  },
  y: {
    label: "Percent Error",
    domain: [-2, 2],
    tickFormat: d3.format(".0%")
  },
  marks: [
    Plot.dot(filtered_allvehplotData, {
      x: "volObs",
      y: "volErrorPct",
      r: 1,
      fill: "rgb(80, 116, 230)",
      fillOpacity: 0.5,
      stroke: "none"
    }),
    Plot.ruleY([2], {
      x1: 0,
      x2: 1,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([1], {
      y1: 1,
      y2: 2,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([1], {
      x1: 1,
      x2: 2.5,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([2.5], {
      y1: 0.5,
      y2: 1.0,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([0.5], {
      x1: 2.5,
      x2: 5,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([5], {
      y1: 0.25,
      y2: 0.50,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([0.25], {
      x1: 5,
      x2: 10,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([10], {
      y1: 0.20,
      y2: 0.25,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([0.20], {
      x1: 10,
      x2: 25,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([25], {
      y1: 0.15,
      y2: 0.20,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([0.15], {
      x1: 25,
      x2: 50,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([50], {
      y1: 0.10,
      y2: 0.15,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([0.10], {
      x1: 50,
      x2: 300,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-2], {
      x1: 0,
      x2: 1,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([1], {
      y1: -1,
      y2: -2,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-1], {
      x1: 1,
      x2: 2.5,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([2.5], {
      y1: -0.5,
      y2: -1.0,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-0.5], {
      x1: 2.5,
      x2: 5,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([5], {
      y1: -0.25,
      y2: -0.50,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-0.25], {
      x1: 5,
      x2: 10,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([10], {
      y1: -0.20,
      y2: -0.25,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-0.20], {
      x1: 10,
      x2: 25,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([25], {
      y1: -0.15,
      y2: -0.20,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-0.15], {
      x1: 25,
      x2: 50,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleX([50], {
      y1: -0.10,
      y2: -0.15,
      stroke: "red",
      strokeWidth: 2
    }),
    Plot.ruleY([-0.10], {
      x1: 50,
      x2: 300,
      stroke: "red",
      strokeWidth: 2
    })
  ]
})

Code

tbEmptyCell2 = 1

Figure 2: Detailed Model vs Observed Comparison of Highway Assignment

Map Comparison

The maps in Figure 3 shows a comparison of segment level model vs observed volumes by vehicle types. Blue represents model lower than observed and red represent model volume higher than observed.

Figure 3: Segment-Level Model vs Observed Volume Comparison by Vehicle Type

Looking at the All Vehicles map, the model volumes are lower than observed for by more than 15,000 vehicles per day for the east side of I-215 and for I-15 through northern Utah County. Model volumes are higher than observed volumes by more than than 15,000 vehicles for I-15 in southern Salt lake County and for I-15 in Utah County between Springville and Spanish Fork. When looking at these areas by vehicle type, the drop in Cars and Light Trucks are actual greater since the Medium Trucks and Heavy Trucks in these areas are greater in the model vs observed. Outside of these areas, the volume differences between model and observed are relatively minor.

The lower arterial model vs observed volumes of Heavy Trucks on 9000 South in Salt Lake County was further investigated. The Heavy Truck observed volume for this roadway seemed much higher than expected for this roadway. The lower volumes are likely due to the observed data and not anything in the model.

Speeds

Comparisons were made between model and observed for a sample of OD pairs. Observed speeds come from sampled data at various time periods in 2019 for the WFRC area using the Google API for reported travel times between coordinates for an origin and destination. Model speeds come from the final network skims that report travel times between all TAZs in the model for each period of the day. The two data sets were joined using a coordinate to TAZ spatial join to locate Google end points to match their respective TAZ.

Code

html`<br/>`

Code

viewof vPeriod = Inputs.select(new Map([['AM','AM'], ['Midday','MD'], ['PM','PM'], ['Evening', 'EV']]), {value: 'AM', label: "Period:"})

speedT = transpose(speed)

speedT_filtered = speedT.filter(function(dataSpd) {
    return vPeriod == dataSpd.Period;
})

Code

html`<h4>Model vs Observed Speeds</h4>`

Code

Plot.plot({
  grid: true,
  width: 460,
  height: 300,
  marginRight: 40,
  x: {
    label: "Observed Speed (mph)",
    domain: [0, 70]
  },
  y: {
    label: "Model Speed (mph)",
    domain: [0, 70]
  },
  marks: [
    Plot.dot(speedT_filtered, {
      x: "spdObs",
      y: "spdMod",
      r: 1,
      fill: "rgb(80, 116, 230)",
      fillOpacity: 0.5,
      stroke: "none"
    }),
    Plot.linearRegressionY(speedT_filtered, {
        x: "spdObs",
        y: "spdMod",
        stroke: "rgb(80, 116, 230)",
        strokeDasharray: "4 4",  // This creates a dashed line pattern,
        strokeWidth: 2 
    })
  ]
})

:::