JournalTOCs

Transportation
Journal Prestige (SJR): 1.911

Citation Impact (citeScore): 3
Number of Followers: 32

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1572-9435 - ISSN (Online) 0049-4488
Published by Springer-Verlag

[2626 journals]

A methodological framework for a priori selection of travel demand
management package using fuzzy MCDM methods
- Abstract: Abstract A robust and scientific selection of appropriate Travel Demand Management (TDM) measures is likely to ensure that the purpose of their implementation is met. The existing methods of TDM measure selection do not often adopt a multi-dimensional approach in their decision-making process. Besides, there is minimal scholarly work on the selection of a ‘push plus pull’ integrated TDM package, even though it has been emphasized in the published literature. These gaps are addressed in our study, which adopts a novel approach of a Multi-Criteria Decision Making (MCDM) methodology. A combination of ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) and ‘Analytical Network Process‘(ANP), along with the VIKOR, which is a ranking method used to select a suitable TDM package, a priori. The demonstration of the proposed methodology is presented for the case study area of Ahmedabad’s old city, which is located in the western state of Gujarat in India. A total of six TDM packages were initially shortlisted as probable contenders for the study area, each package consisting of one coercive (push) and one attracting (pull) measure. The MCDM tool ‘VIKOR’ was used to derive the ranking of these packages based on their performance in seven different criteria. The DEMATEL based ANP method was used to determine the weights of these criteria by comparing the influence of criteria over each other. The uncertainty and subjectivity involved in the entire process was taken into account by employing fuzzy logic into our proposed methodology. The research contribution of the study is a methodological framework that can assist policymakers in the a priori selection of a TDM package. The outputs from the study are twofold—(a) the Influence Network Relationship Map (INRM) and (b) the ranking of the TDM packages for the case study area. The INRM explains the relations between dimensions as well as between criteria within each dimension. Additionally, it classifies the dimension and criteria into cause and effect groups, which can help policymakers in developing a better understanding of the TDM implementation process. In our case study, the TDM package of congestion pricing and public bike sharing was the top ranked package. The most influential dimension was political feasibility, followed by private participation and public acceptability. In addition, effectiveness, investment attractiveness, and system resistance were the most influencing criteria within their respective dimensions.
  PubDate: 2021-01-12
Comparative analysis of escalator capacity at metro stations: theory
versus practice
- Abstract: Abstract The studies on escalators have mainly reported the walking speeds of passengers or their passenger handling capacity. Theoretical capacity is reported as a constant value for a given escalator speed, but is never achieved in the field. This creates practitioners’ dilemma regarding augmentation of the facility as some capacity will always remain available even under high passenger flows. This paper explores the issue and proposes a reference capacity for examining the effective utilisation of the escalator. The capacity is estimated with stand-only and stand-walk etiquette. The data available in the literature and from the video graphed information at metro stations in Delhi, India is used to examine the effect of step occupancy, walking speed of passengers and their proportion in the flow. It is observed that walking-factor and step occupancy is higher in developing countries. Walking factor is estimated as 0.70–0.74 and step occupancy as 0.80–1.70. The occupancy on walking side is estimated as one passenger per two steps as compared to the reported value of one per three steps. This indicates that the flow conditions on escalators in India are more restrictive as compared to other countries. The comparison of field capacity with reference capacity has indicated that the reference capacity can be used for the evaluation of actual flow condition on escalators.
  PubDate: 2021-01-12
Online and in-store purchase behavior: shopping channel choice in a
developing economy
- Abstract: Abstract Developing economies are still in the stage of e-commerce deployment, unlike in developed countries, where online shopping is already a common purchase channel. This research aims to assess the purchasing behaviors of end-consumers in regard to two alternative shopping channels: in-store and online, within a developing economy. A revealed-preference survey was conducted to collect the in-store and online purchase activity of households in Ukraine. The data collected presents the purchase channel choices of end-consumers for eleven commodities in two categories: experience goods and search goods. A descriptive analysis of in-store and online shopping was made, with an evaluation of average purchase cost and time expenditures for shopping, travel, and delivery processes. A pooled binomial logit model was then developed to assess the purchase channel choice based on a Random Utility Maximization Theory. The estimated values of a marginal probability effect are presented, and the significance levels of attributes influencing purchase channel choice are evaluated. The marginal probability effect is found to be greater for shopping cost than for time attributes for most of the studied commodity channel choices. The sensitivity assessment for purchase cost, delivery and travel time revealed that first-priority goods such as medicine, food, clothing and shoes depended more on the attributes’ values variation than other commodities considered in the study. The comparison of this research’s results with other studies has shown a higher importance of shopping cost than time attributes for channel choice decisions in the case of the developing economy.
  PubDate: 2021-01-12
Counting people in the crowd using social media images for crowd
management in city events
- Abstract: Abstract City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.
  PubDate: 2021-01-12
Assessing motorist behavior during flash floods in Tucson, Arizona
- Abstract: Abstract Weather events often force motorists to drive in unsafe conditions or alter travel plans. Both decisions are imbued with costs that drivers may minimize by developing adaptation behaviors that facilitate safe travel. This study examines motorist behavior in Tucson, where urban flash floods are potentially avoided by strategies such as changing the route or trip timing. A sample of 108 residents completed a stated adaptation questionnaire regarding which behaviors they take to avoid floods prior to departure and upon encountering a flood. A multiple regression analysis tests whether behaviors depend on demographic characteristics, familiarity with flood locations, and previous experience entering floodwaters. This study also considers the types of information that people seek upon encountering a flood. The results highlight the importance of flexibility and familiarity for motorists’ decision making. Fewer adaptation behaviors are used for urgent commute trips, but route switching is common for the most frequent trips to work, school, and home. Pre-trip behaviors account for most differences because most respondents use adaptation behaviors upon flood encounter, but age, ethnicity, and previous experience with floods are key factors. Differences in flood avoidance behaviors appear to be linked to travel flexibility, not just propensity to take risks. These results also highlight the significant potential for communicating alternate routes to help motorists reduce flood exposure and other point-location hazards while minimizing travel disruption.
  PubDate: 2021-01-04
A novel perspective to enhance the role of TPB in predicting green travel:
the moderation of affective-cognitive congruence of attitudes
- Abstract: Abstract Successful and sustained interventions to obtain travel behavior change would be achieved based on a thorough understanding of individual’s decision-making process on travel. To narrow the gap between the theory of planned behavior (TPB) and green travel behavior, this study extends classical TPB by accommodating the moderating effect of affective-cognitive congruence of attitudes. Based on a cluster analysis utilizing affective and cognitive attitudes towards private driving, four groups are obtained, each characterized by different extent of feelings/emotions for private driving and of evaluations/beliefs about the consequences on environment due to car-use. A multi-group structural equation model analysis explicitly confirms the moderating role of affective-cognitive congruence of attitudes, given that the structural relations between overall attitude, subjective norm, active and passive PBC, green travel intention, as well as green travel behavior significantly differ depending on the extent and direction of the congruence between affective and cognitive attitudes. It is expected that the empirical findings might be useful for transport administrators to maximize the effects of their limited resources and funds.
  PubDate: 2021-01-03
Exploring partnership between transit agency and shared mobility company:
an incentive program for app-based carpooling
- Abstract: Abstract How should public transit agencies deliver mobility services in the era of shared mobility' Previous literature recommends that transit agencies actively build partnerships with mobility service companies from the private sector, yet public transit agencies are still in search of a solid empirical basis to help envision the consequences of doing so. This paper presents an effort to fill this gap by studying a recent experiment of shared mobility public–private partnership, the carpool incentive fund program launched by King County Metro in the Seattle region. This program offers monetary incentives for participants who commute using a dynamic app-based carpooling service. Through descriptive analysis and a series of logistic regression models, we find that the monetary incentive to encourage the use of app-based carpooling generates some promising outcomes while having distinctive limitations. In particular, it facilitates the growth of carpooling by making carpooling a competitive commuting option for long-distance commuters. Moreover, our evidence suggests that the newly generated carpooling trips mostly substitute single-occupancy vehicles, thus contributing to a reduction of regional VMT. The empirical results of this research will not only help King County Metro devise its future policies but also highlight an appealing alternative for other transit agencies in designing an integrated urban transportation system in the era of shared mobility.
  PubDate: 2021-01-03
Transportation infrastructure improvement and real estate value: impact of
level crossing removal project on housing prices
- Abstract: Abstract This paper studies the impact of removing the level crossing, which constitutes traffic hazard to the society, on house prices by conducting a quasi-natural experiment using the Level Crossing Removal Project (LXRP) implemented by the Victoria state government in Australia since 2015. Using a difference-in-differences method, we analyzed the changes in housing prices due to the improvement of transportation infrastructure, gauging the LXRP’s impact on house and unit submarkets separately. We found that the prices for house and unit markets increased significantly after the removal of level crossings, with the value uplift decreasing with distance from the removal site. This paper contributes to the existing literature by adding an empirical study related to the enhancement of infrastructure aiming to improve the traffic safety in the urban context. Unlike previous studies, this study examines the effect of improvement projects for existing infrastructure and provides relevant implications to improve the efficiency of investing public resources in infrastructure improvement.
  PubDate: 2021-01-02
GIS-based identification and visualization of multimodal freight
transportation catchment areas
- Abstract: Abstract To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.
  PubDate: 2021-01-02
Impacts of improvements in rural roads on household income through the
enhancement of market accessibility in rural areas of Cambodia
- Abstract: Abstract Rural areas in low-income countries often face severe poverty typically caused by insufficient accessibility to basic facilities. Improvements in rural roads are expected to reduce poverty although the mechanism has not been investigated sufficiently. This study empirically analyzes the impacts of rural road improvements implemented from 2012 to 2014 in Cambodia, highlighting local residents’ accessibility to local markets. This study assumes two causal relationships: rural road improvements have upgraded the accessibility and travel frequency to local markets, and the upgraded accessibility and travel frequency to local markets have led to a growth in local residents’ income. The hypotheses are statistically tested with a dataset developed through a questionnaire survey conducted in three areas in 2016. The dataset contains responses from 400 local residents to questions concerning their social attributes, livelihoods, travel modes, travel frequency, and time/cost of travels to the basic facilities. The quasi-experimental design incorporating a difference-in-differences design and an inverse possibility of treatment weighting approach revealed that the improvements in rural roads did not affect travel time nor travel cost but significantly enhanced travel frequency to local markets, and that an increase in the travel frequency to local markets and travel time savings significantly contributed to the households’ income growth. The results suggest that the improvement of seasonal reliability in accessing local markets through an introduction of all-weather roads could be critical to enhance household income, particularly in areas where agriculture is a leading industry and weather conditions are unstable across seasons.
  PubDate: 2021-01-02
Mobile phone data in transportation research: methods for benchmarking
against other data sources
- Abstract: Abstract The ubiquity of personal cellular phones in society has led to a surging interest in using Big Data generated by mobile phones in transport research. Studies have suggested that the vast amount of data could be used to estimate origin–destination (OD) matrices, thereby potentially replacing traditional data sources such as travel surveys. However, constructing OD matrices from mobile phone data (MPD) entails multiple challenges, and the lack of ground truth hampers the evaluation and validation of the estimated matrices. Furthermore, national laws may prohibit the distribution of MPD for research purposes, compelling researchers to work with pre-compiled OD matrices with no insight into the methods used. In this paper, we analyse a set of such pre-compiled OD matrices from the greater Oslo area and perform validation procedures against several sources to assess the quality and robustness of the OD matrices as well as their usefulness in transportation planning applications. We find that while the OD matrices correlate well with other sources at a low resolution, the reliability decreases when a finer level of detail is chosen, particularly when comparing shorter trips between neighbouring areas. Our results suggest that coarseness of data and privacy concerns restrict the usefulness of MPD in transport research in the case where OD matrices are pre-compiled by the operator.
  PubDate: 2021-01-02
Autonomous vehicles in mixed motorway traffic: capacity utilisation,
impact and policy implications
- Abstract: Abstract In upcoming years, the introduction of autonomous vehicles (AVs) will reshape the transport system. The transition from a regular to an autonomous transport system, however, will take place over many years and lead to a long period with a mixed driving environment where AVs and regular vehicles (RVs) operate side by side. The purpose of this study is to investigate how the utilisation of the road capacity degrades as a function of heterogeneity in congested motorways. The analysis is based on a dedicated traffic simulator, which enables the investigation of complex dynamic spillback from congestion while allowing for different degrees of heterogeneity. The representation of autonomous vehicles is based on a modified intelligent driver model (IIDM) presented by Treiber et al. (Phys Rev E 62(2):1805–1824, 2000) and Treiber and Kesting (Traffic flow dynamics, Springer, Heidelberg, 2013), while the behaviour of drivers of RVs relies on a stochastic version of the IIDM. Three main conclusions stand out. Firstly, it is shown that in an idealised environment in which AVs operate alone, a substantially improved capacity utilisation can be attained. Secondly, when drivers of RVs are mixed with AVs, capacity utilisation degrades very fast as a function of the share of RVs. Thirdly, it is shown that the improved capacity utilisation of AVs comes in the form of reduced travel time and increased throughput, with indications that travel time reductions are the most important. From a strategical planning perspective, the results underline that dedicated lanes are preferable to attain the positive effects of AVs. Specifically, we compare a stylised situation with three lanes with a share of 33% AVs to a situation with two regular lanes and a single dedicated AV lane. The latter represents a tripling in consumer surplus all other things being equal.
  PubDate: 2021-01-02
Modelling user satisfaction in public transport systems considering
missing information
- Abstract: Abstract Collecting data to obtain insights into customer satisfaction with public transport services is very time-consuming and costly. Many factors such as service frequency, reliability and comfort during the trip have been found important drivers of customer satisfaction. Consequently, customer satisfaction surveys are quite lengthy, resulting in many interviews not being completed within the aboard time of the passengers/respondents. This paper questions as to whether it is possible to reduce the amount of information collected without a compromise on insights. To address this research question, we conduct a comparative analysis of different Ordered Probit models: one with a full list of attributes versus one with partial set of attributes. For the latter, missing information was imputed using three different methods that are based on modes, single imputations using predictive models and multiple imputation. Estimation results show that the partial model using the multiple imputation method behaves in a similar way to the model that is based on the full survey. This finding opens an opportunity to reduce interview time which is critical for most customer satisfaction surveys.
  PubDate: 2020-12-01
Heterogeneity in marginal value of urban mobility: evidence from a
large-scale household travel survey in the Greater Toronto and Hamilton
Area
- Abstract: Abstract The value of mobility is an unresolved question in transportation economics literature. The advent of ride-hailing services and the emergence of mobility as a service (MaaS) place increased pressure on the research community to develop methods to consider this question. We provide one of the first efforts to quantify the value of mobility using a consistent econometric approach. A series of discrete choice models are estimated for car ownership and residential density choices. The decision to purchase an additional vehicle is a concrete manifestation of the marginal value of travel vis-a-vis the desire to make additional trips. The proposed framework has the benefit of employing a single utility function, thus removing the need to set a reference alternative. Models are estimated with a large household travel survey for the Greater Toronto and Hamilton Area, which provides a close approximation to the true population. We estimate separate values of mobility by household composition, providing evidence for a high degree of heterogeneity. Results are examined in the context of MaaS and it is found that the value of mobility is much higher in suburban areas than suggested in previous research. Model results provide strong evidence for potential social exclusion with the widespread adoption of MaaS. The methods explored in this paper show great promise for quantifying the value of mobility and we present recommendations for additional research in this direction.
  PubDate: 2020-12-01
Validation of automatic passenger counting: introducing the t-test-induced
equivalence test
- Abstract: Abstract Automatic passenger counting (APC) in public transport has been introduced in the 1970s and has been rapidly emerging in recent years. Still, real-world applications continue to face events that are difficult to classify. The induced imprecision needs to be handled as statistical noise and thus methods have been defined to ensure that measurement errors do not exceed certain bounds. Various recommendations for such an APC validation have been made to establish criteria that limit the bias and the variability of the measurement errors. In those works, the misinterpretation of non-significance in statistical hypothesis tests for the detection of differences (e.g. Student’s t-test) proves to be prevalent, although existing methods which were developed under the term equivalence testing in biostatistics (i.e. bioequivalence trials, Schuirmann in J Pharmacokinet Pharmacodyn 15(6):657–680, 1987) would be appropriate instead. This heavily affects the calibration and validation process of APC systems and has been the reason for unexpected results when the sample sizes were not suitably chosen: Large sample sizes were assumed to improve the assessment of systematic measurement errors of the devices from a user’s perspective as well as from a manufacturers perspective, but the regular t-test fails to achieve that. We introduce a variant of the t-test, the revised t-test, which addresses both type I and type II errors appropriately and allows a comprehensible transition from the long-established t-test in a widely used industrial recommendation. This test is appealing, but still it is susceptible to numerical instability. Finally, we analytically reformulate it as a numerically stable equivalence test, which is thus easier to use. Our results therefore allow to induce an equivalence test from a t-test and increase the comparability of both tests, especially for decision makers.
  PubDate: 2020-12-01
Estimation of origin–destination matrices using link counts and
partial path data
- Abstract: Abstract After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.
  PubDate: 2020-12-01
How do they get by without cars' An analysis of travel characteristics
of carless households in California
- Abstract: Abstract In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.
  PubDate: 2020-12-01
Modelling traffic flows and estimating road travel times in transportation
network under dynamic disturbances
- Abstract: Abstract Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation.
  PubDate: 2020-12-01
Assessment of the transit ridership prediction errors using AVL/APC data
- Abstract: Abstract The disparity between actual and forecasted transit ridership has been an important area of study and a concern for researchers for several decades. In order to decrease the discrepancy caused by model property errors, a number of studies focus on better representation of difficult-to-measure cost functions and incorporation of behavioral variables in mode choice models. In spite of the improvement, some gaps still remain in practical applications, particularly for large-scale regional travel forecasting models which are zone-based and aggregated. With automated data collection systems including Automatic Vehicle Location/Automatic Passenger Count (AVL/APC), modellers have great potential to use these technologies as new or complementary data sources to reliably estimate system performances and observed transit ridership. In particular, an opportunity exists to explore model prediction errors at a more disaggregate spatial scale. In this paper, using AVL/APC data, a method to effectively identify and evaluate the source of transit ridership prediction errors is proposed. Multinomial regression models developed in this research produce equations for mode choice prediction errors as a function of: measurable but omitted market segmentation variables in current mode choice utility functions; and newly quantifiable attributes with new data sources or techniques including quality of service variables. Further, the proposed composite index can systematically evaluate and prioritize the major source of prediction errors by quantifying total magnitudes of prediction error and a possible error component. The outcomes of the research can serve as foundation towards more reliable and accurate mode choice models and ultimately enhanced transit travel forecasting.
  PubDate: 2020-12-01
The evaluation of large cycling infrastructure investments in Glasgow
using crowdsourced cycle data
- Abstract: Abstract The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013–2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data.
  PubDate: 2020-12-01