Scoring and classifying regions via multimodal transportation networks

Abstract

In order to better understand the role of transportation convenience in location preferences, as well as to uncover transportation system patterns that span multiple modes of transportation, we analyze 500 locations in the Tokyo area using properties of their multimodal transportation networks. Multiple sets of measures are used to cluster regions by their transportation features and to classify them by their synergistic properties and dominant mode of transportation. We use twelve measures collected at five different radii for five distinct combinations of transportation networks to rank locations by their transportation characteristics. We introduce an additional 114 scores derived from the 300 measures to assess, among other things, access to public transportation, the effectiveness of each mode of transportation, and synergies among the modes of transportation. Additionally, we leverage those scores to classify our locations as being train-centric, bus-centric, or car-centric and to uncover geographic patterns in these characteristics. We find that business hubs, despite having low populations, are so conveniently reachable via train and road systems that they consistently achieve the highest sociability and convenience scores. Suburban regions have more serviceable bus systems, but lower connectivity overall resulting in lower reachable populations despite greater local populations. Even though Tokyo has the largest and densest public transportation system in the world we find that the road network consistently dominates the train and bus networks for all accessibility measures.

The Aspiration for Happy Train Journey: Commuters’ Perception of the Quality of Intercity Rail Services

“…The attributes were first checked for multi-collinearity using Spearman correlation. This revealed medium to weak correlation among the demographic attributes, as shown in Figure 4 [67,68]. Hence, all demographic attributes were considered as independent variables in M2.…”

“…As shown in Table 7, the suitability of model M2 is assessed, respectively, by the following goodness of fit indicators: −2 log-likelihood, Pearson chi-square, Cox and Snell R square, and Nagelkerke R square. The R square values indicate reliability of the model, where greater R square value indicates good correlation between the data [67]. However, as argued by some researchers, low R Square values in logistic regression are the norm and thus this indicator is not recommended as a measure of model goodness [62,70].…”

“…Hence, M1 has been developed using proportional odds model, where each subcategory has been compared with a reference sub-category. Multi-collinearity test using Spearman correlation revealed medium to weak correlation among the demographic attributes, as shown in Figure 3 [67,68]. Hence, all demographic attributes were considered as independent variables in M1.…”

“…[67,68 all demographic attributes were considered as independent variables in M1.Fig veals moderate correlation in Age-Occupation, Age-Travel Frequency, Occupation-T quency, and Travel Frequency-Income Level. Legend: Y1: Gender Y2: Age Y3: Occupation Y4: Travel frequency (visits/month) Y5: Income level (BDT/month)…”

Modeling the Public Transport Networks: A Study of Their Efficiency

“…Spearman's method was used to calculate correlations. e importance of each predictor in the model was estimated calculating the absolute value of the t − statistics [71], whose definition has been presented in 2.2.1 e importance of predictors is shown in Table 4.…”