Commuter Behavior Research Articles

Effects of Current Distortion on DC Link Inductor and Capacitor Lifetime in Variable Frequency Drive Connected to Grid With Active Harmonic Filter

In this research article, new investigation and comprehensive analysis is done on the current distortion effects of dc link inductor and capacitor in variable frequency drive (VFD). Shunt active filter (SAF) is the most prominent solution for harmonic mitigation at the point of common coupling in the power distribution system. However, SAF connectivity reduces the grid impedance ( ZTH ) seen at the input of six pulse diode bridge rectifier in VFD. Commutation behavior of the diode rectifier is changed due to this grid impedance variation resulting in increased harmonic current distortion (THD i ) at the input of the rectifier bridge. This effect increases the ripple current of dc capacitor, dc link inductor losses, and diode rectifier conduction losses. This study investigates the drive dc link inductor and capacitor lifetime reduction with and without SAF, under various grid operating conditions of 3% voltage unbalance and 8% predistortion. Moreover, the high frequency (2–9 kHz) impact on dc link inductor core and copper losses is detailed. A 250 kW VFD with SAF of 180 A capacity is used for test measurement under various operating conditions. Current distortion effects are analyzed, and test results are compared with MATLAB simulation. Grid impedance variation due to SAF and its effect on the reliability of the VFD front-end components were not covered in detail in any of the earlier research. Lifetime estimation of dc link inductor and capacitor due to grid impedance variation is one among the significant outcomes of this investigation.

Impacts of COVID-19 on the Transport Sector and Measures as Well as Recommendations of Policies and Future Research: Report on India

This report discusses the implications of various phases of lockdown as well as unlocking phases aimed at the gradual ramp up of the economy during the ongoing COVID-19 pandemic in India. In this regard, the consequences and changes in the travel behavior of commuters by air, rail and various forms of road based transport are discussed. The report also presents an overview of the impact of the pandemic on freight traffic as well as its impact on road safety. To derive the related findings, the relevant studies carried out across India by different organizations (including CSIR - CRRI) during the ongoing pandemic have been briefly discussed and compared with pre-COVID-19 scenario. This is primarily aimed at improving the transport scenario to a sustainable level post COVID which is expected to be reached earliest by the middle of 2021. In this context, a summary of the applicable guidelines issued by Ministry of Home and Urban Affairs (MoHUA) with the specific emphasis on road based transport has been presented in the concluding section by highlighting the immediate, medium as well as long term strategies. These strategies provide insight into the policies that need to be in place for recuperation of the slumped transport sector due to COVID-19 through the implementation of various forms of sustainable solutions presented in the report.

Choice behavior of commuters' rail transit mode during the COVID-19 pandemic based on logistic model

To understand whether commuters will take rail transit during the COVID-19 pandemic, a logistic regression model was constructed from three aspects of personal attributes, travel attributes and perception of COVID-19 based on 559 valid questionnaires. The results show that: occupation, commuting tools before the COVID-19 pandemic, walking time from residence to the nearest subway station, the possibility of being infected in private car and the possibility of being infected in public transport have significant influence on the commuters' choice of rail transit. Self-employed people and freelancers, commuters who used non-public transport before the COVID-19 pandemic, and commuters who take longer to walk from their residences to the nearest subway station are less likely to commute by rail transit during the COVID-19 pandemic. Commuters who think that the risk of being infected with the virus in public transport is higher have a lower probability of choosing rail transit. The confidence in bus/subway/taxi/taxi-hailing of commuters who do not choose to commute by rail transit during the COVID-19 pandemic is not high. The study of this paper can provide reference for the formulation of urban rail transit control measures during the COVID-19 pandemic, so as to formulate more perfect measures to ensure the safety of the returning workers.

Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown

The outbreak of COVID-19 pandemic has resulted in change in both commute and personal travel patterns. Though, in India, lockdown was implemented from 25th March 2020, due to self-awareness and pandemic risk perception, change in commuter behavior was observed from the beginning of March 2020. The period from 15th to 24th March 2020 is considered as the transition phase of COVID-19 outbreak in India (i.e., between no lockdown and lockdown period). This study attempts to use a decision tree approach to investigate the modal preference of 1542 commuters in association with socio-economic and travel characteristics, and safety perceptions with respect to public and private modes during transition to lockdown due to COVID-19 in India. About 41% of commuters stopped traveling during the transition to lockdown phase, 51.3% were using the same mode of transport and 5.3% of commuters shifted from public to private mode. The study findings reported different interactions of factors influencing the decision to use public or private modes of transport for daily commuting during pandemic situations like COVID-19. Interestingly, safety perceptions (associated with personal health) of commuters did not play a significant role in their mode choice behavior during the transition phase. Though people perceived public transportation as unsafe over personal vehicle use, the actual commute patterns did not validate this due to a possible reason that commuters do not have enough alternative modes. Given the uncertainties in the decision making of the commuters regarding their travel behavior due to physical distancing, the insights from this study are important to policymakers and local transport authorities to understand the change in travel patterns.

Scheduling preferences, parking competition, and bottleneck congestion: A model of trip timing and parking location choices by heterogeneous commuters

This study examines the effects of a time-varying congestion toll and a location-dependent parking fee on the behavior of heterogeneous commuters and their commuting costs. To this end, we develop a model of departure time and parking location choices by heterogeneous commuters and characterize its equilibrium. By comparing the equilibrium with and without pricing policies, we obtain the following results: (1) imposing a parking fee and expanding parking capacity may concentrate the temporal distribution of traffic demand, thereby exacerbating traffic congestion; (2) the expansion of parking capacity does not necessarily lead to a Pareto improvement when a parking fee is not imposed; (3) the social optimum is achieved by combining a parking fee with a congestion toll; and (4) the revenue obtained from pricing of roads and parking exactly equals the costs for optimal bottleneck and parking capacities, respectively; that is, the self-financing principle holds separately for bottleneck capacity and parking capacity.

Crowdsourcing as a Social Interaction Tool to Stimulate Sustainable Transportation Mode Use

Background: In this paper, a crowd-based social interaction framework is developed to assess the potential increase in the use of sustainable transportation modes – such as walking, bicycling and public transit. Methods: The empirical data were used to validate mode shift behaviors for 77 participants from California State University Long Beach. Data collection spanned over two phases, Phase I followed by Phase II. Each study phase lasted a month. Participants used one of the four modes – personal car, walking, bicycling and public transit - to arrive at the university campus. During Phase I, a control group was created, and individual mode choice of participants were obtained. Individual participants in Phase II were assigned short-encrypted distinct names and were asked to post a daily comment on the quality of experience using the mode that was used to arrive at the campus. The participants were asked to post the comments over a “Twitter” page that was used as the crowdsourcing platform for this study. The encrypted name masked the individual identity of the user. Analysis at the end of Phase II showed that there was an overall mode-shift of almost 19% of personal car users to other sustainable modes of walking, bicycling and transit. Results: Results show very important policy implications of using crowdsourcing as a social interaction tool to influence mode choice behavior of commuters, especially among college students and young adults. Conclusion: A crowd-based social interaction framework is developed to assess potential increase in the use of sustainable transportation modes – such as walking, bicycling, and public transit. Results showed that providing advanced information on traffic and parking problems can result in a mode shift to active transportation modes.

The Effects of Moral Norms, Social Norms and Pro-Environmental Attitude on Travel Behavior In Malaysia

Public transportation helps to reduce air pollution that creates negative impacts on work performance, work quality, and productivity. The study investigates the effects of moral norms, social norms, and pro-environmental attitudes on the travel behavior of public transport commuters. A survey was carried in Greater Kuala Lumpur, and a total of 904 usable questionnaires were analyzed using Partial Least Squares. The findings showed that a pro-environmental attitude had a weak relationship to travel behavior, but moral norms played a strong instrumental influence when commuters chose public transport. Recommendation and suggestions provided. Keywords: Travel Behavior; Moral Norms; Social Norms; Pro-environmental Attitude. eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i14.2196

Modal choice analysis for a linear monocentric city with battery electric vehicles and park-charge-ride services

The limited endurance mileage of battery electric vehicles (BEVs) affects mode choice of commuters inevitably. To help facilitate BEV charging and improve accessibility of transit stations, the park-charge-ride (PCR) services have been implemented in some cities. This study develops a deterministic continuum equilibrium model for a monocentric city to analyze the modal choice behavior of commuters in a transportation system with BEVs and PCR services. Theoretical analysis is conducted for the model and some equilibrium properties are presented. The positive influence for setting PCR facilities is confirmed from theoretical point of view. It is also found that in the transportation systems with PCR services, a location exists that commuters living closer to CBD should choose railway as their only transportation mode. By deploying the discrete approximation approach, numerical examples are given to demonstrate the model. The impact of factors including initial state of charge of the battery electric vehicles, in carriage crowding cost of the railway transit are studied via numerical analysis.

Do housing tenure and public transport provision matter in automobile use in bedroom suburban communities? Evidence from Beijing

To understand the factors shaping commute behaviour, we need to relate dwelling tenure and location to commuting decisions, especially at the expanding edges of emerging megacities such as those in China. This paper proposes a cross-sectional examination of housing choice, public transport provision and automobile related behaviour to understand the underlying mechanism for travel behaviour. Residential communities at the urban periphery (n = 25) were randomly sampled by stratified house price in three suburban extension areas of Beijing. Respondents intercepted in the public environment (n = 1224) provided information on household characteristics, housing tenure, and commute choices over a timeframe. The relationships among these variables were examined using partial least squares structural equation modelling. Research findings showed that public transport provision had positive effect on car ownership, and housing choice including dwelling tenure and job-housing distance had significant impact on automobile ownership and commute as well. Within housing choice, tenure status had positive significant effect on job-housing distance. Public transport provision was also found to have significant impact on housing choice behaviours. While previous work on commuting behaviour has concentrated on transport-related factors, these results indicate that travel outcomes are related to a considerable extent to urban development factors that are susceptible to policy control.

Mode Choice Modeling Using Adaptive Data Collection for Different Trip Purposes in Mumbai Metropolitan Region

The issues of traffic and transportation planning in cities such as Mumbai are wellknown, but they are not well understood. Understanding mode choice behavior of travelers is critical for transportation planners. This paper focuses on understanding this behavior and understanding the factors that influences mode choice in Mumbai Metropolitan Region. An online survey system was designed to enable the users to fill in the revealed choices they made for a particular trip. This research examines the impact of socio-demographic attributes of travelers along with trip characteristics, such as travel time and waiting time on mode choice behavior. The data include 253 trips undertaken by 178 persons for three major trip purposes: mandatory, shopping, and recreational was captured. Multinomial logit (MNL) and mixed logit (MXL) models are developed to portray mode choice behavior. The models are able to capture the transit-captive behavior of commuters. The MXL model slightly outperformed the MNL model both, in terms of statistical fit and prediction success rate. This research exemplifies that choice of mode is often governed by purpose associated with the activity. Considering that there has been a huge modal shift from public transit to private vehicles (cars and two-wheelers) in Mumbai over the last decade, estimation of mode choice models are important for policy formulation.

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then they would be valuable assets to various service providers to explore business opportunities, to study commuter behavior for better transport management, which in turn benefits the general public for day-to-day commuting. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy, as users’ trajectories may be misused to discover sensitive information, such as home locations, their children’s school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns. None of these prior works address the scalability issue, since it is a newly occurring problem brought by the significantly increasing adoption of location-based services. In this article, we conquer these two challenges by designing a novel parallel trajectory anonymization algorithm that achieves scalability, strong privacy protection, and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using MapReduce and Spark on real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the centralized approaches.

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

Road traffic environments are highly dynamic and volatile with a multitude of roadside and external environmental factors contributing to its dynamicity. Apart from infrastructure-related means such as traffic lights, planned and unplanned road events and different road networks, a core component which contributes towards the traffic environment is the human factor which is heavily overlooked in the current studies. Due to diverse travel patterns of day-to-day activities, the commuter behaviour is directly depicted in traffic patterns providing an opportunity to further explore human behaviours using road traffic. Conducting such analysis would reveal different commuter behavioural patterns that can be used for optimization and timely management of operations. However, to conduct such real-time behaviour analysis, large volumes of high-frequency data are required with high granularity, as well as, a suitable technology to manage such data. Addressing these needs, we propose an environment-driven commuter behavioural model that can be used to elucidate diverse behaviours in road traffic environments. We conceptualized, designed and developed an artificial intelligence based commuter behaviour profiling framework to detect diverse commuter behavioural profiles, fluctuating and routine patterns among commuters using traffic flow profiling and travel trajectory analysis. We evaluated the framework using 190 million data points captured from the Bluetooth sensor network of the Melbourne arterial road network, in the state of Victoria in Australia. The results demonstrate that traffic flow profiling of the proposed framework can provide insights on recurrent commuter behaviours that are distinct to a selected area with a high granularity. Moreover, traffic trajectory analysis provides insights on non-recurrent behaviours such as accidents with regard to how such incidents impact the dynamics of the network and how the impact is propagated through the network. Besides road traffic management, the proposed framework will enable real-time decision-making when planning road infrastructure and support decision-making of government and business entities to optimize operations.

Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure

This paper examines the charging behavior of 7,979 plug-in electric vehicle (PEV) owners in California. The study investigates where people charge be it at home, at work, or at public location, and the level of charging they use including level 1, level 2, or DC fast charging. While plug-in behavior can differ among PEV owners based on their travel patterns, preferences, and access to infrastructure studies often make generalizations about charging behavior. In this study, we explore differences in charging behavior among different types of PEV owners based on their use of charging locations and levels, we then identify factors associated with PEV owner’s choice of charging location and charging level. We identified socio-demographic (gender and age), vehicle characteristics, commute behavior, and workplace charging availability as significant factors related to the choice of charging location.

What Drives Commuter Behaviour?

Abstract Laura Dawkins describes a project to encourage commuters to leave their cars at home

Pricing equilibrium of transportation systems with behavioral commuters

We study Wardrop equilibrium in a transportation system with profit-maximizing firms and heterogeneous commuters. Standard commuters minimize the sum of monetary costs and equilibrium travel time in their route choice, while commuters choose the route with minimal idle time. Three possible scenarios can arise in equilibrium: A pooling scenario where all commuters make the same transport choice; A separating scenario where different types of commuters make different transport choices; A partial pooling scenario where some standard commuters make the same transport choice as the oblivious commuters. We characterize the equilibrium existence condition, derive equilibrium flows, prices and firms' profits in each scenario, and conduct comparative analyses on parameters representing route conditions and heterogeneity of commuters, respectively. The framework nests the standard model in which all commuters are standard as a special case, and also allows for the case in which all commuters are oblivious as the other extreme. Our study shows how the presence of behavioral commuters under different route conditions affects equilibrium behavior of commuters and firms, as well a the equilibrium outcome of the transportation system.

The Mode Most Traveled: Parking Implications and Policy Responses

A disproportionate number of Americans drive to work alone – at the individual state level, the figure ranges from a low of 58% in New York to 85% in Alabama. What factors explain this travel behavior and what sort of policy responses are required to effect changes? In responding to this question, we used multiple test beds and made the case for using a demand side management approach to remedy parking problems particularly observed in cities central business districts. To this end, we provide an overview of travel behavior and information on policy levers by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental United States. Building off the result that revealed San Francisco as an outlier, we examine the efficacy of modifying parking rates, via elasticity measures, to influence the demand for parking by developing a two-stage panel data regression optimization model for managing parking in the City of San Francisco. A key contribution of the research is deriving these price elasticities of parking demand estimates using panel data methods. Coefficient estimates from the panel data regression are used to fit a linear prediction model that is the primary input to the optimization model. The balance of the thesis focuses on parking information by discussing the design and implementation of ParkPGH, a novel smart parking application that provides real time and predictive information on garage parking availability in downtown Pittsburgh. At its core is a predictive model that uses as input historical parking, weather and event data to provide estimates of available parking spaces. We provide an example of the model implementation using data from the Theater Square garage where we utilize neural network-based predictors and multiple net searches to generate both continuous and binary estimates of parking availability. Provision was made for the binary classifier given the need to reduce the possibility of Type II errors.

Profiling commuters’ travel behavior in the pacific states of the continental U.S.

We develop a more nuanced understanding of commuters’ travel mode choices by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental US. These profiles are created by utilizing the US Census Public Use Microdata Sample (PUMS) data. The microdata sample set allows for the estimation of fine-grained models that showcase how individual commuters make travel mode choices. Our results show appreciable locational variation in mode choices and statistically significant differences in commuting profile across and within population segments. A key revealing finding demonstrates that across the three states analyzed, the total number of vehicles driven for any day of the week could be reduced by up to 10 million assuming the commuting patterns observed in San Francisco applies to the rest of the states. We conclude with insights and policy implications provided by the study on making transportation-related infrastructure decisions.

Analysis of multi-modal commute behavior with feeding and competing ridesplitting services

Public transit is an essential travel mode in many urban areas. Emerging dynamic ridesplitting programs provided by transportation network companies (TNCs) can be a double-edged sword to public transit. On the one hand, the program provides convenient services to solve first- and last-mile problems. On the other hand, long-distance ridesplitting services may also draw passengers away from public transit. In this paper, we propose a network model to analyze multi-modal commute behavior with ridesplitting programs as both feeders and competitors to public transit, which is with limited accessibility to passengers. The ridesplitting priority and ridesplitting fare ratio (i.e., ridesplitting fare over non-ridesplitting fare) are incorporated as operational strategies of the TNC. Through numerical studies, we find that a significant number of public transit passengers will shift to long-distance ridesplitting services under low fare ratios; and a high ridesplitting priority can lead to a demand drawback for long-distance ridesplitting, which raises public transit ridership. To maintain public transit ridership, the TNC needs to keep a high fare ratio and a high priority; meanwhile, the number of short-distance ridesplitting orders can also decrease dramatically, which may lead to a loss in unit time revenue of the TNC. We note that a win–win condition can be reached through a separated discount strategy for first- and last-mile ridesplitting services. Such a strategy can both increase the number of short-distance ridesplitting orders for the TNC and boost transit ridership for the government, as well as provide low-cost services to passengers.

What we can learn from five naturalistic field experiments that failed to shift commuter behaviour.

Across five field experiments with employees of a large organization (n = 68,915), we examined whether standard behavioural interventions ('nudges') successfully reduced single-occupancy vehicle commutes. In Studies 1 and 2, we sent letters and emails with nudges designed to increase carpooling. These interventions failed to increase carpool sign-up or usage. In Studies 3a and 4, we examined the efficacy of other well-established behavioural interventions: non-cash incentives and personalized travel plans. Again, we found no positive effect of these interventions. Across studies, effect sizes ranged from Cohen's d = -0.01 to d = 0.05. Equivalence testing, using study-specific smallest effect sizes of interest, revealed that the treatment effects observed in four out of five of our experiments were statistically equivalent to zero (P < 0.04). The failure of these well-powered experiments designed to nudge commuting behaviour highlights both the difficulty of changing commuter behaviour and the importance of publishing null results to build cumulative knowledge about how to encourage sustainable travel.

Identification of Determinant Factors Influencing Modal Choice Behavior and Satisfaction Level of Commuters: A Case of Rajshahi City

The term commuter is defined as a person who travels some distance to work on a regular basis Asensio et al. [1]. A large number of commuters are attracted.