The value of additional data for public transport origin–destination matrix estimation

Passenger origin–destination data is an important input for public transport planning. In recent years, new data sources have become increasingly common through the use of the automatic collection of entry counts, exit counts and link flows. However, collecting such data can be sometimes costly. The value of additional data collection hence has to be weighed against its costs. We study the value of additional data for estimating time-dependent origin–destination matrices, using a case study from the London Piccadilly underground line. Our focus is on how the precision of the estimated matrix increases when additional data on link flow, destination count and/or average travel distance is added, starting from origin counts only. We concentrate on the precision of the most policy-relevant estimation outputs, namely, link flows and station exit flows. Our results suggest that link flows are harder to estimate than exit flows, and only using entry and exit data is far from enough to estimate link flows with any precision. Information about the average trip distance adds greatly to the estimation precision. The marginal value of additional destination counts decreases only slowly, so a relatively large number of exit station measurement points seem warranted. Link flow data for a subset of links hardly add to the precision, especially if other data have already been added.

Spatial variation in bicycling: a retrospective review of travel survey data from Greater Melbourne, Australia

Understanding spatial variation in bicycling within cities is necessary to identify and address inequities. We aimed to explore spatial variation in bicycling and explore how bicycling rates vary across population sub-groups. We conducted a retrospective analysis of household travel survey data in Greater Melbourne, Australia. We present a descriptive analysis of bicycling behaviour across local government areas (LGAs; n=31), with a focus on quantifying spatial variation in the number and proportion of trips made by bike, and by age, sex and trip distance. Associations between the proportion of infrastructure that had provision for biking and the proportion of all trips made by bike were analysed using linear regression. Overall, 1.7% of all trips were made by bike. While more than half (53.2%) of all trips were less than 5km, only 2% of these trips were by bike. Across LGAs, there was considerable variation in the proportion of trips made by bike (range: 0.1% to 5.7%). Mode share by females was 35.0%, and this varied across LGAs from 0% to 49%. Tor each percentage increase in the proportion of infrastructure that had provision for biking, there was an associated 0.2% increase in the proportion of trips made by bike (coefficient = 0.20; SE = 0.05; adjusted R2 = 0.38). While we observed a low bicycle mode share, more than half of all trips were less than 5 km, demonstrating substantial opportunity to increase the number of trips taken by bike.

On Time-Dependent Trip Distance Distribution with For-Hire Vehicle Trips in Chicago

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

Improved Prediction of Total Energy Consumption and Feature Analysis in Electric Vehicles Using Machine Learning and Shapley Additive Explanations Method

Electric vehicles (EVs) have emerged as the green energy alternative for conventional vehicles. While various governments promote EVs, people feel “range anxiety” because of their limited driving range or charge capacity. A limited number of charging stations are available, which results in a strong demand for predicting energy consumed by EVs. In this paper, machine learning (ML) models such as multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector regression (SVR) were used to investigate the total energy consumption (TEC) by the EVs. The independent variables used for the study include changing real-life situations or external parameters, such as trip distance, tire type, driving style, power, odometer reading, EV model, city, motorway, country roads, air conditioning, and park heating. We compared the ML models’ performance along with the error analysis. A pairwise correlation study showed that trip distance has a high correlation coefficient (0.87) with TEC. XGBoost had better prediction accuracy (~92%) or R2 (0.92). Trip distance, power, heating, and odometer reading were the most important features influencing the TEC, identified using the shapley additive explanations method.

Impacts of autonomous vehicles on traffic volume: scenarios based on survey data for the City of Karlsruhe

Autonomous vehicles will become a significant influence in the field of traffic and transportation. To determine the possible impact of fully automated traffic, this thesis analyzes trip-pattern data for the City of Karlsruhe, Germany. Based on survey data from the year 2012, the traveled distances are calculated in Karlsruhea baseline scenario as well as two competitive scenarios: best-case and worst-case. The database is analyzed for the most emerging trip patterns in three areas of the City of Karlsruhe. Trip data, including trip distance and mode choice, are analyzed by trip purpose and individual groups (based on employment status). By modifying the average trip distance, mode choice and trip patterns based on literature reviewed information, the consequences of autonomous vehicles are estimated. The study shows, that autonomous vehicles have the potential to reduce traffic (best-case), but on the other hand, could approximately double the overall traveled vehicle distances (worst-case).

Impacts of autonomous vehicles on traffic volume: scenarios based on survey data for the City of Karlsruhe

Autonomous vehicles will become a significant influence in the field of traffic and transportation. To determine the possible impact of fully automated traffic, this thesis analyzes trip-pattern data for the City of Karlsruhe, Germany. Based on survey data from the year 2012, the traveled distances are calculated in Karlsruhea baseline scenario as well as two competitive scenarios: best-case and worst-case. The database is analyzed for the most emerging trip patterns in three areas of the City of Karlsruhe. Trip data, including trip distance and mode choice, are analyzed by trip purpose and individual groups (based on employment status). By modifying the average trip distance, mode choice and trip patterns based on literature reviewed information, the consequences of autonomous vehicles are estimated. The study shows, that autonomous vehicles have the potential to reduce traffic (best-case), but on the other hand, could approximately double the overall traveled vehicle distances (worst-case).

Optimal Fleet Deployment Strategy: Model the Effect of Shared E-Bikes on Bike-Sharing System

Following the bike-sharing system, the shared e-bike becomes increasingly popular due to the advantage in speed, trip distance, and so forth. However, limited research has investigated the impact of the introduction of shared e-bikes on the existing bike-sharing systems. This paper aims to study the effect of shared e-bikes on the traditional bike-sharing system and determine the optimal fleet deployment strategy under a bimodal transportation system. A stochastic multiperiod optimisation model is formulated to capture the demand uncertainty of travelers. The branch-and-bound algorithm is applied to solve problem. A 15-station numerical example is applied to examine the validity of the model and the effectiveness of the solution algorithm. The performance of integrated e-bike and bike-sharing system has been compared with the traditional bike-sharing system. The impacts of the charging efficiency, fleet size, and pricing strategy of e-bike-sharing system on the traditional bike-sharing system have been examined.

Does Revitalizing the Center of Mid-Sized French Cities Reduce GHG Emissions from Commuting?

Mid-sized cities are usually considered in the literature to be shrinking cities. Some policies promote right-sizing and others promote revitalization. The relationship between land-use planning and mobility having been established, the present research issue is focused on whether a policy of revitalizing the centers of mid-sized cities is favorable to low-carbon mobility. Our study investigates commuting trips through two indicators: commuting trip distance and car modal share. The increase in total population, the increase in the number of jobs per resident, the decrease in the unemployment rate, the increase in the rate of executives, the increase in the rate of working people in the population and the decrease in the residential vacancy rate all come from the censuses of 2006 and 2016. Statistical models based on individuals in 113 mid-sized cities, in which sociodemographic variables are introduced, show that at the level of agglomerations, no indicator has a simultaneously positive effect in the center and in the urban periphery. No indicator is entirely positive or negative on GHG emissions from commuting trips. While the increase in GHG emissions from commuting trips between 2006 and 2016 is significant in mid-sized cities (18%), a shift toward shrinking city centers is insufficient to change this trajectory.