Mode Choice

Mode Share

The mode choice model was calibrated to observed mode shares from the 2012 Household Travel Survey and the 2019 Transit On-Board Survey. The results of this calibration effort are shown in Figure 1. All mode shares were calibrated to within 5% of observed data.

Code

html`<br/>`

Code

viewof plotSelect    = Inputs.select(new Map([[nameModeMnMShare,nameModeMnMShare],
                                              [nameModeATShare ,nameModeATShare ],
                                              [nameModeDAShare ,nameModeDAShare ],
                                              [nameMode23Share ,nameMode23Share ],
                                              [nameModeTransit ,nameModeTransit ],
                                              [nameModeAccess  ,nameModeAccess  ]
                                             ]), {value: nameGroup, label: "Mode Group:"})
viewof periodSelect  = Inputs.select(new Map([['Daily', 'DY']]), {value: namePeriod, label: "Time Period:"});
//viewof periodSelect  = Inputs.select(new Map([['Daily', 'DY'], ['Peak', 'PK'], ['Off-Peak', 'OK']]), {value: namePeriod, label: "Time Period:"});
viewof purposeSelect = Inputs.select(new Map([['All Purposes', 'All']]), {value: 'TripPurpose', label: "Trip Purpose:"});
// viewof purposeSelect = Inputs.select(new Map([['All Purposes', 'All'], ['Home-Based Work', 'HBW'], ['Home-Based College', 'HBC'], ['Home-Based Other', 'HBO'], ['Non-Home Based', 'NHB']]), {value: 'TripPurpose', label: "Trip Purpose:"});

dataLT = transpose(dataLong)
dataTT = transpose(dataTable)

filtered_data = dataLT.filter(function(dataL) {
    return plotSelect == dataL.Group &&
           periodSelect == dataL.Period &&
           purposeSelect == dataL.TripPurpose;
})
table_filtered_data = dataTT.filter(function(dataTT) {
    return plotSelect == dataTT.Group &&
           periodSelect == dataTT.Period &&
           purposeSelect == dataTT.TripPurpose;
})

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

xDomain = {
    switch(plotSelect) {  // assuming all data points have the same Mode
        case nameModeMnMShare:
            return ['Motorized', 'Non-Motorized'];
            break;
        case nameModeATShare:
            return ['Auto', 'Transit'];
            break;
        case nameModeDAShare:
            return ['Drive Alone', 'Shared Ride'];
            break;
        case nameMode23Share:
            return ['Shared Ride 2 Occupants', 'Shared Ride 3+ Occupants'];
            break;
        case nameModeTransit:
            return ['Local Bus', 'Core Bus', 'Express Bus', 'BRT', 'LRT', 'CRT'];
            break;
        case nameModeAccess:
            return ['Walk', 'Drive'];
            break;
        default:
            return [];  // or set some sensible default
    }
}

Code

html`
<br/>
<table>
    <thead>
    <tr>
        ${[plotSelect, "Model", "Observed", "Difference"].map((d, i) => 
        html`<th style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; ${i === 0 ? "width: 185px;" : ""}'>${d}</th>`)}
    </tr>
    </thead>
    <tbody>
    ${table_filtered_data.map(row => html`
        <tr style='border-bottom: 1px solid lightgrey;'>
        ${["Mode", "Model", "Observed", "Difference"].map((d, i) => 
            html`<td style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; ${i === 0 ? "width: 185px;" : ""}'>${row[d]}</td>`)}
        </tr>`)}
    </tbody>
</table>`

Code

key = Legend(chart.scales.color, {title: "Data Source"})

chart = GroupedBarChart(filtered_data, {
    x: d => d.Mode,
    y: d => d.Percent,
    z: d => d.DataSource,
    xDomain: xDomain,
    yLabel: "Percent",
    yDomain: [0,1],
    zDomain: ['Model','Observed'],
    width: 400,
    height: 250,
    colors: ["#376092", "#77933c"]
})

Code

bEmtptyCell=1

Figure 1: Mode Share Calibration Results

Code

html`<br/><br/>`

Transit Trips and Boardings

Transit trips were validated to the 2019 Transit On-Board Survey and 2019 observed boarding data. To facilitate model calibration, transit trips, boardings, and transfers were validated by the model’s hierarchical mode. Boardings were also validated based on the mode where the boarding was actually observed. Transit validation results are shown in Figure 2.

Total transit trips and boardings were calibrated to within 5% of observed data (trips 1.6%, boardings -0.8%). Overall transfers were all within an acceptable range.

Transit trips and boardings by mode were calibrated to acceptable ranges for modes with significant ridership. Modes with low ridership were allowed to have a higher difference when compared to observed data if calibrating to increase base year accuracy resulted in too large alternative specific constants (i.e. over calibrating these modes). However, the following suggestions may help guide when using the model and interpreting model results:

• BRT validation results were low (between -6.7% and -12.6%). However, only one BRT route (UVX) was available in 2019 to calibrate this mode. Partly due to this, additional rounds of calibration to improve BRT resulted in large constants. This in turn would have the effect of making the base year validation better but overpredicting BRT in future forecasts, particularly as there is significantly more BRT in future plan phases. The decision was made to allow BRT to show lower than expected ridership in the earlier years of the model in favor of more reasonable BRT future-year forecasts.
• Core Route has a similar issue to BRT where only 1 Core Route (3500 S) was available in 2019 to calibrate this mode. Core Route had a much lower ridership in 2019 (roughly a tenth of the BRT ridership) which tends to show more extreme validation results. Core Route validation showed a similar pattern to BRT with lower trips and boardings (-14.4% and -14.0%, respectively) with the exception that the model was overpredicting total Core Route boardings (24.5%) resulting from slightly too many trips using this mode to transfer to a higher mode (LRT or CRT). Similar to BRT, the decision was made to not over calibrate this mode, in particular as there is also significantly more Core Route in future plan phases, in favor of more reasonable Core Route future-year forecasts.
• Express Bus trip and boarding validation results are higher than desired (8.5% and 11.6%, respectively). However, Express Bus ridership in 2019 is not significant and Express Bus service is expected to decrease in future plan phases. Note that the model underpredicts overall boardings (-17.2%) largely due to the observed data showing trips in the downtown area are transferring from other modes (e.g. CRT) to use Express Bus more as a local downtown circulator. The model does not capture this behavior.

Code

html`<br/>`

Code

viewof bPlotSelect = Inputs.select(new Map([['Trips by Hierarchical Mode', 'Trips by Hierarchical Mode'], ['Boardings by Hierarchical Mode', 'Boardings by Hierarchical Mode'], ['Transfer Ratio by Hierarchical Mode','Transfer Ratio'], ['Boardings by Observed Mode','Boardings by Mode Surveyed']]), {value: nameGroup, label: "Category"})
//viewof metric = Inputs.radio(new Map([["Difference", "Difference"], ["% Difference", "% Difference"]]), {value: "Difference", label: "View:"})

dataBLC = transpose(boardChart)
filtered_bDataC = dataBLC.filter(function(dataBLC) {
    return bPlotSelect == dataBLC.Title &&
           "Value" == dataBLC.View;
})
dataBTT = transpose(boardTable)
filtered_bDataT = dataBTT.filter(function(dataBTT) {
    return bPlotSelect == dataBTT.Title;
})

Code

html`
<br/>
<table>
    <thead>
    <tr>
        ${["Mode", "Model", "Observed", "Difference", "% Difference"].map((d, i) => {
            const widths = ['90px', '60px', '70px', '75px', '90px']; // Define the widths
            return html`<th style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; width: ${widths[i]};'>${d}</th>`;
        })}
    </tr>
    </thead>
    <tbody>
        ${filtered_bDataT.map(row => {
            const isBold = row['Mode'] === 'Total';
            return html`<tr style='border-bottom: 1px solid lightgrey;'>
                ${["Mode", "Model", "Observed", "Difference", "% Difference"].map((d, i) => {
                    const widths = ['90px', '60px', '70px', '75px', '90px']; // Define the widths
                    return html`<td style='text-align: ${i === 0 ? "left" : "right"}; padding: 5px; width: ${widths[i]}; font-weight: ${isBold ? 'bold' : 'normal'};'>${row[d]}</td>`;
                })}
            </tr>`;
        })}
    </tbody>
</table>`

Code

key2 = Legend(bChart.scales.color, {title: "Data Source"})

bChart = GroupedBarChart(filtered_bDataC, {
    x: d => d.Mode,
    y: d => d.ViewValue,
    z: d => d.DataSource,
    xDomain: ['Local Bus', 'Core Bus', 'Express Bus', 'BRT', 'LRT', 'CRT'],
    yLabel: "Value",
    zDomain: ['Model','Observed'],
    width: 500,
    height: 225,
    colors: ["#376092", "#77933c"]
})

Code

tbEmptyCell = 1

Figure 2: Trips and Boardings by Mode Surveyed - Model vs. Observed Comparison

Code

html`<br/>`