Trip Distribution

The model used the 2012 Household Travel Survey to calibrate trip lengths for internal and external person trips. Trips were calibrated based on generalized cost trip length frequencies and validated to time and distance trip length frequencies and average trip lengths. The validation results are shown in Figure 1 and Figure 2. Trip lengths by time and distance met the validation targets and aligned closely with observed data.

No observed data was available for internal and external commercial vehicle calibration. CV trips were calibrated based on previous model estimation and validated relative to person work and other trip lengths.

Average Trip Length Validation

Code

viewof varSelect = Inputs.select(new Map([['Generalized Cost','GC'], ['Distance', 'Dist'], ['Time', 'Time']]), {value: 'Variable', label: "Variable"})

viewof purpSelect  = Inputs.select(new Map([['Internal Trip Purposes', 'MainPurposes'], ['External Trip Purposes', 'Externals']]), {value: 'PurpType', label: "Purpose Set"});

Figure 1: Average Trip Length

Trip Length Frequency Validation

The following trip length frequency charts have been updated to exclude intrazonal trips. The distance charts only include data beyond 0, the time charts only include data beyond 4, and the general cost charts only include data beyond 4. Please note that these values were calculated at a mark where the majority of the trip would not be intrazonal trips. Intrazonal trips were analyzed separately.

Code

optionsTripPurp = 
[
    ['Home-Based Work'                   , 'HBW'    ],
    ['Home-Based Shopping'               , 'HBShp'  ],
    ['Home-Based Other'                  , 'HBOth'  ],
    ['Home-Based School - Primary'       , 'HBSchPr'],
    ['Home-Based School - Secondary'     , 'HBSchSc'],
    ['Non-Home-Based Work'               , 'NHBW'   ],
    ['Non-Home-Based Non-Work'           , 'NHBNW'  ],
    ['Light CV'                          , 'LT'     ],
    ['Medium CV'                         , 'MD'     ],
    ['Heavy CV'                          , 'HV'     ],
    ['Internal-to-External'              , 'IX'     ],
    ['External-to-Internal'              , 'XI'     ]
]

viewof vTripPurp = Inputs.select(new Map(optionsTripPurp), {value: 'HBW', label: "Trip Purpose:"})

viewof vTlfType = Inputs.select(new Map([['Generalized Cost','GC'],['Distance','Dist'],['Time','Time']]), {value: 'GC', label: "Variable:"})

Code

freqT = transpose(freq)

freqT_filtered_Obs = freqT.filter(function(freqData) {
    return vTripPurp == freqData.TRIPPURP &&
    vTlfType == freqData.TLFTYPE &&
    'freqObs' == freqData.freqSource;
})

freqT_filtered_Mod = freqT.filter(function(freqData) {
    return vTripPurp == freqData.TRIPPURP &&
    vTlfType == freqData.TLFTYPE &&
    'freqMod' == freqData.freqSource;
})

Code

dataX = transpose(xvals)
filtered_x = dataX.filter(function(dx) {
    return vTripPurp == dx.Purpose &&
           vTlfType == dx.TLF;
})

maxXValue = Math.max(...filtered_x.map(function(dx){ 
    return dx.maxX;
}));
minXValue = Math.max(...filtered_x.map(function(dx){ 
    return dx.minX;
}));

Code

Plot.plot({
  grid: true,
  height: 420,   // << this is the key fix
  x: {
    label: 'Distance / Time / Cost',
    domain: [minXValue, maxXValue]  // <-- force start at 4 to exclude intrazonals
  },
  y: {
    label: 'Frequency',
    tickFormat: f => `${Math.round(f * 100)}%`
  },
  marginRight: 40,
  marginLeft: 60,
  marginTop: 40,
  marginBottom: 50,
  style: { fontSize: 15 },
  marks: [
    Plot.line(freqT_filtered_Mod, {
      x: 'BIN',
      y: 'freq',
      stroke: "#376092",
      strokeWidth: 3
    }),
    Plot.line(freqT_filtered_Obs, {
      x: 'BIN',
      y: 'freq',
      stroke: "#77933c",
      strokeWidth: 3
    }),
    Plot.frame({ strokeOpacity: 0.2 })
  ]
});

Figure 2: Trip Length Frequency - Beginning

Intrazonal Trip Percentage

Figure 3: Intrazonal trip percentage between model and observed.

Productions & Attractions

Code

dc_final = {
  const data = transpose(trips_master_data);
  return data.map(d => ({
    ...d,
    tmeMod: d.mod,
    tmeObs: d.obs,
    tmeErrorPct: d.obs > 0 ? (d.mod - d.obs) / d.obs : 0
  })).filter(d => d.tmeObs > 0 || d.tmeMod > 0);
}

// 2. Selectors
viewof vGeo_final = Inputs.select(["District", "County"], {label: "Geography Level:", value: "District"})

// This dropdown updates its options based on what's available in the data
viewof vPurp_final = Inputs.select(
  Array.from(new Set(dc_final.map(d => d.PURP))).sort(), 
  {label: "Trip Purpose:"}
)

// 3. Final Filtered Data
dc_filtered_final = dc_final.filter(d => d.Geo === vGeo_final && d.PURP === vPurp_final)

chartMax_final = d3.max(dc_filtered_final, d => Math.max(d.tmeObs, d.tmeMod)) * 1.05

Code

// CHART 4a: Model vs Observed Trips
Plot.plot({
  grid: true,
  width: 460,
  height: 380,
  marginRight: 50,
  caption: html`<h4>4a. ${vPurp_final} Trips (${vGeo_final} Level)</h4>`,
  x: { label: "Observed Trips", domain: [0, chartMax_final] },
  y: { label: "Modeled Trips", domain: [0, chartMax_final] },
  marks: [
    Plot.link([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x1: 0, y1: 0,
      x2: (k) => (k <= 1 ? chartMax_final : chartMax_final / k),
      y2: (k) => (k <= 1 ? chartMax_final * k : chartMax_final),
      strokeOpacity: (k) => k === 1 ? 1 : 0.2,
      stroke: "gray",
      strokeWidth: (k) => k === 1 ? 2 : 1
    }),
    // Labels for Fan Lines
    Plot.text([0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4], {
      x: (k) => (k <= 1 ? chartMax_final : chartMax_final / k),
      y: (k) => (k <= 1 ? chartMax_final * k : chartMax_final),
      text: (k) => k === 1 ? "Equal" : d3.format("+.0%")(k - 1),
      textAnchor: "start", dx: 6, fill: "gray", fontSize: 10
    }),
    Plot.dot(dc_filtered_final, {
      x: "tmeObs", y: "tmeMod", r: 3.5,
      fill: "rgb(80, 116, 230)", fillOpacity: 0.5,
      tip: true,
      title: (d) => 
        `${d.Geo}: ${d.p_Loc} to ${d.a_Loc}\n` +
        `Observed: ${d3.format(",.0f")(d.tmeObs)}\n` +
        `Modeled: ${d3.format(",.0f")(d.tmeMod)}`
    }),
    Plot.linearRegressionY(dc_filtered_final, {
      x: "tmeObs", y: "tmeMod",
      stroke: "rgb(80, 116, 230)", strokeDasharray: "4 4"
    })
  ]
})

Code

// CHART 4b: Percent Error
Plot.plot({
  grid: true,
  width: 460,
  height: 380,
  caption: html`<h4>4b. ${vPurp_final} Percent Error</h4>`,
  x: { label: "Observed Trips", domain: [0, chartMax_final] },
  y: { label: "Percent Error", domain: [-2, 2], tickFormat: d3.format(".0%") },
  marks: [
    Plot.ruleY([0], { stroke: "#000", strokeWidth: 1.5 }),
    Plot.dot(dc_filtered_final, {
      x: "tmeObs", y: "tmeErrorPct", r: 3.5,
      fill: "rgb(80, 116, 230)", fillOpacity: 0.5,
      tip: true,
      title: (d) => 
        `${d.Geo}: ${d.p_Loc} to ${d.a_Loc}\n` +
        `Observed: ${d3.format(",.0f")(d.tmeObs)}\n` +
        `Error: ${d3.format("+.1%")(d.tmeErrorPct)}`
    }),
    // The Validation "Staircase"
    Plot.line([[0, 2.0], [1, 2.0], [1, 1.0], [2.5, 1.0], [2.5, 0.5], [5, 0.5], [5, 0.25], [10, 0.25], [10, 0.20], [25, 0.20], [25, 0.15], [50, 0.15], [50, 0.10], [chartMax_final, 0.10]], 
      { stroke: "gray", strokeWidth: 1.5, strokeDasharray: "2 2", curve: "step-after" }),
    Plot.line([[0, -2.0], [1, -2.0], [1, -1.0], [2.5, -1.0], [2.5, -0.5], [5, -0.5], [5, -0.25], [10, -0.25], [10, -0.20], [25, -0.20], [25, -0.15], [50, -0.15], [50, -0.10], [chartMax_final, -0.10]], 
      { stroke: "gray", strokeWidth: 1.5, strokeDasharray: "2 2", curve: "step-after" })
  ]
})