Commute Trips Research Articles

Support for market-based and command-and-control congestion relief policies in Latin American cities: Effects of mobility, environmental health, and city-level factors.

Public support for the implementation of congestion relief policies is critical for the policies’ technical and political success. To identify the personal, social, and city-level factors associated with higher acceptance towards such policies, this study uses a 2016 survey of 8178 residents from 11 cities across 10 Latin American countries collected by the Development Bank of Latin America (Corporación Andina de Fomento or CAF). We examined support for two demand-side approaches to managing the traffic congestion externality: congestion pricing – a market-based approach, and driving restrictions or bans – a command-and-control approach. Logit regression models show that personal mobility such as owning or using a private vehicle during a respondent’s main commute trip are associated with decreased support, while higher congestion delay in one’s commute and having a young child recently diagnosed with respiratory problems increases support for either congestion relief policy. In addition, residents of cities with higher levels of median annual particulate matter and with prior experience with traffic bans expressed higher support for either policy. Residents of cities with higher income inequality supported only driving restrictions; while those of cities with higher GDP per capita had lower support only for congestion pricing. To improve the public acceptance of congestion relief policies in Latin America, policy makers could: (1) explicitly seek to mitigate the costs it brings on individuals by investing in substitutes like public transportation; (2) promote the personal and social environmental and health benefits; (3) consider beginning with temporary, pilot programs; and in the case of driving restrictions, (4) take into account city-specific conditions related to income inequality that may influence public support for the policies.

The indirect effect of the built environment on travel mode choice: A focus on recent movers

Many studies have demonstrated that the built environment has a strong impact on people's travel mode choice. However, the built environment also influences elements such as travel distance and car ownership, which might be the true predictors of which travel modes are chosen. In this study, we analyse the effects of changes in residential neighbourhood on changes in travel mode (for commute trips and leisure trips), both directly and indirectly through changes in car ownership, travel distances and travel attitudes. This study applies a structural equation modelling approach using quasi-longitudinal data from 1650 recently relocated residents in the city of Ghent, Belgium. Results indicate that the built environment has strong direct effects on active leisure trips and car use. However, distance (for car use) and attitudes (for active travel) were found to be important mediating variables. In sum, the effect of the built environment on travel mode choice might be more complex than commonly assumed as it partly seems mediated by travel distance and travel attitudes.

A Traffic Demand Analysis Method for Urban Air Mobility

This paper explores the addressable market for Urban Air Mobility (UAM) as a multi-modal alternative in a community. To justify public investment, UAM must serve urban mobility by carrying a significant portion of urban traffic. We develop a traffic demand analysis method to estimate the maximum number of people that can benefit from UAM, for a given use case, in a metropolitan region. We apply our method to about three hundred thousand cross-bay commute trips in the San Francisco Bay Area. We estimate the commuter demand shift to UAM under two criteria of flexibility to travel time savings and three criteria of vertiport transfer times. Our results indicate that even for commuters with high value of time, and long transfer times at vertiports, almost forty-five percent of demand would benefit from UAM when the roads are highly congested. Even when the roads are mostly free, about three percent of the demand can benefit from UAM with the right combination of commuter flexibility and transfer times. Finally, our method also produces the number, location and distribution of demand over vertiports that can support value proposition, policy-making and technology research for UAM.

The benefits of autonomous vehicles for community-based trip sharing

This work reconsiders the concept of community-based trip sharing proposed by Hasan et al. (2018) that leverages the structure of commuting patterns and urban communities to optimize trip sharing. It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners. In the considered problem, each rider specifies a desired arrival time for her inbound trip (commuting to work) and a departure time for her outbound trip (commuting back home). In addition, her commute time cannot deviate too much from the duration of a direct trip. Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%.This paper studies the potential benefits of autonomous vehicles in further reducing the number of vehicles needed to serve all these commuting trips. It proposes a column-generation procedure that generates and assembles mini routes to serve inbound and outbound trips, using a lexicographic objective that first minimizes the required vehicle count and then the total travel distance. The optimization algorithm is evaluated on a large-scale, real-world dataset of commute trips from the city of Ann Arbor, Michigan. The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%. These results demonstrate the significant potential of autonomous vehicles for the shared commuting of a community to a common work destination.

Assessing motorist behavior during flash floods in Tucson, Arizona

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.

Public Transportation Demand Analysis: A Case Study of Metropolitan Lagos

Modelling, simulation, and forecasting offer a means of facilitating better planning and decision-making. These quantitative approaches can add value beyond traditional methods that do not rely on data and are particularly relevant for public transportation. Lagos is experiencing rapid urbanization and currently has a population of just under 15 million. Both long waiting times and uncertain travel times has driven many people to acquire their own vehicle or use alternative modes of transport. This has significantly increased the number of vehicles on the roads leading to even more traffic and greater traffic congestion. This paper investigates urban travel demand in Lagos and explores passenger dynamics in time and space. Using individual commuter trip data from tickets purchased from the Lagos State Bus Rapid Transit (BRT), the demand patterns through the hours of the day, days of the week and bus stations are analysed. This study aims to quantify demand from actual passenger trips and estimate the impact that dynamic scheduling could have on passenger waiting times. Station segmentation is provided to cluster stations by their demand characteristics in order to tailor specific bus schedules. Intra-day public transportation demand in Lagos BRT is analysed and predictions are compared. Simulations using fixed and dynamic bus scheduling demonstrate that the average waiting time could be reduced by as much as 80%. The load curves, insights and the approach developed will be useful for informing policymaking in Lagos and similar African cities facing the challenges of rapid urbanization.

The Time-of-Day Travel Demand Elasticity Paradox

Driving demand and congestion are known to peak throughout the day, but temporal heterogeneity in travel demand elasticities is often overlooked. I estimate how the fuel economy elasticity of travel demand — a measure of the rebound effect from fuel economy standards — varies across peak and off-peak periods. The results present a paradox. Drivers are most elastic during peak demand periods. Mode switching for shorter commute trips appears to drive the heterogeneity, and the pollution benefits of fuel economy standards are shown to be more than offset by exacerbated congestion costs due to the timing of the rebound effect.

Logistic models explaining the determinants of biking for commute and non-commute trips in Lahore, Pakistan

Logistic models explaining the determinants of biking for commute and non-commute trips in Lahore, Pakistan

Exposures to multiple air pollutants while commuting: Evidence from Zhengzhou, China

Daily commutes can contribute disproportionately to overall daily exposure to urban air pollutant concentrations. It is important to understand factors that contribute to the variability of exposures during commutes to develop strategies to mitigate exposure. We used low-cost Fairsense PMS1003 sensors to monitor exposure to multiple air pollutants: PM2.5, PM10, SO2, CO, O3 and NO2, across four dominant transport modes: bike, taxi, subway and bus, on prescribed commuting routes covering a wide range of environments representative of downtown Zhengzhou during the morning and evening rush hours. Zhengzhou is a city in China that faces severe air quality challenges.We compared exposures in the different modes and evaluated factors that contributed to variability in exposure to the different pollutants during commute trips. There were statistically significant differences in exposures across commuting modes for most pollutants. Commuting in air-conditioned taxis had the lowest exposure to PM, but highest exposures to some primary gaseous pollutants (SO2 and CO).In a multivariate analysis, the travel mode generally explained the greatest variability in pollutant exposure, in comparison to route, time of day (morning/evening), and background pollutant concentrations. When breathing rate was taken into account to compute pollutant doses, cycling emerged as the commuting mode with the highest inhaled dose.

Factors Influencing Teleworking Productivity – a Natural Experiment during the COVID-19 Pandemic

Of 2174 surveyed adults who were teleworking following the implementation of a Covid-19 work-from-home policy, 23.8% reported an increase in productivity, 37.6% no change, and 38.6% a decrease in productivity compared to working at their prior workplace. After controlling for feelings of depression and anxiety likely caused by pandemic-related circumstances, the socioeconomic characteristics associated with no change or an increase in productivity after shifting to teleworking included being older; not employed in higher education; having lower education attainment; and not living with children. Respondents with longer commute trips in single-occupancy vehicles prior to teleworking were more likely to be more productive but those with longer commute by walking were not. Lifestyle changes associated with increased productivity included better sleep quality, spending less time on social media, but more time on personal hobbies.

The Commute Trip-Sharing Problem

Parking pressure has been steadily increasing in cities as well as on university and corporate campuses. To relieve this pressure, this paper studies a carpooling platform that would match riders and drivers while guaranteeing a ride back and exploiting spatial and temporal locality. In particular, the paper formalizes the commute trip-sharing problem (CTSP) to find a routing plan that maximizes ride sharing for a set of commute trips. The CTSP is a generalization of the vehicle routing problem with routes that satisfy time-window, capacity, pairing, precedence, ride-duration, and driver constraints. The paper introduces two exact algorithms for the CTSP: a route-enumeration algorithm and a branch-and-price algorithm. Experimental results show that on a high-fidelity real-world data set of commute trips from a midsize city, both algorithms optimally solve small and medium-sized problems and produce high-quality solutions for larger problem instances. The results show that carpooling, if widely adopted, has the potential to reduce vehicle usage by up to 57% and decrease vehicle miles traveled by up to 46% while incurring only a 22% increase in average ride time per commuter for the trips considered.

Does Building Highways Reduce Traffic Congestion?

In a seminal study, Duranton and Turner (2011) finds evidence that points to the existence of the fundamental law of highway congestion in the US. They build a causal model using an instrumental variable (IV) approach that yields an estimate of 1.03 for the elasticity of vehicle miles traveled (VMT) to the stock of interstate highways in US metropolitan areas. The result means that government efforts to alleviate traffic congestion by expanding capacity are likely to fail — any increase in the stock of highways is accompanied by a commensurate increase in VMT, leaving travel times unaffected. In this article, we explore the impact of unobserved heterogeneity on the fundamental law. We begin by using a simple partial equilibrium model to demonstrate how metropolitan statistical areas (MSAs) that are identical in most respects but have different initial congestion levels respond differently to added capacity due to individual differences. These differences in MSAs gives rise to heterogeneity in the elasticity of VMT to capacity. We derive conditions under which the elasticity decreases with the initial congestion level. We then revisit the empirical analysis in Duranton and Turner (2011) using the instrumental variable quantile regression (IV-QR) model due to Chernozhukov and Hansen (2005, 2006, 2008). The IV-QR model allows us to incorporate variation in the elasticity due to the presence of unobserved differences across MSAs. Moreover, it allows us to evaluate the impact of changes in the stock of interstate highways on the entire conditional distribution of VMT, not just the impact on the conditional mean as in Duranton and Turner (2011). The IV-QR estimates show that as predicted by the simple partial equilibrium model, the elasticity declines as one goes up the quantile ladder, being more than one at the lower quantiles and less than one at the higher quantiles. The median IV-QR estimate being close to one. The IV-QR model implies that among observationally identical cities, expanding road capacity can lower the number of cities experiencing severe congestion, although the mean or median congestion levels are likely to remain constant. We also estimate the impact of increased road capacity on the unconditional distribution of VMT using the generalized quantile regression (GQR) model due to Powell (Forthcoming). The GQR estimates mirror the IV-QR estimates, but their conclusions are starker at the upper quantiles: building highways have no statistically significant impact on VMT at the highest quantiles. The GQR results imply that building roads can lower the total number of cities having different observed characteristics experiencing severe congestion levels. We further explore the mechanisms that drive the empirical findings by running simulations using a spatial general equilibrium model with an extensive road network calibrated to the Greater Los Angeles (LA) Region. In the general equilibrium model, besides commute trips, consumers have to travel to different zones to acquire consumption goods. The model considers route choice in the network and mode choice. Building roads affect road traffic through three channels: the total amount of consumption, mode choice, and the substitutions among goods sold at different locations. We find that the elasticity of VMT to capacity in LA is 0.321, and the elasticity decreases consistently with the initial congestion level. We report the welfare effect and the changes in other travel-related variables. Our results have important policy implications in that they show that while building roads is unlikely to change the mean or median congestion level, it can reduce the number of cities experiencing high congestion levels.

Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong

• Estimated the causal economic impact of an off-peak fare discount in Hong Kong. • Applied the difference-in-difference method to control for confounding bias. • Found the aggregate effect to be small but statistically significant and heterogeneous. • Responsiveness was governed by travel costs and crowding. • Produced novel causal estimates of trip rescheduling elasticities. This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy.

Performance and Intrusiveness of Crowdshipping Systems: An Experiment with Commuting Cyclists in The Netherlands

Crowdshipping systems are receiving increasing attention in both industry and academia. Different aspects of crowdshipping (summarized as platform, supply, and demand) are investigated in research. To date, the mutual influence of crowdshipping platform design and its supply side (with participating crowdshippers) has not yet been thoroughly investigated. This paper addresses this mutual influence by investigating the relations between shipping performance and intrusiveness to daily trips of commuters who voluntarily act as cycle couriers. In an experiment in The Hague, cyclists were asked to transport small parcels during a simulated daily commuting routine. The grid of commuting trips acted as a relay network to move parcels to their individual destinations. All the movements of the parcels were recorded by GPS trackers. The analysis indicates that a higher degree of complexity of rules in crowdshipping systems can lead to better system performance. Meanwhile, it also imposes higher intrusiveness, as participants need to deviate more from their routines of daily, uninterrupted trips. The case also suggests that a well-designed crowdshipping system can increase system performance without having to ask too much from crowdshippers. This study provides reference to better design such systems, and opens up directions for further research that can be used to provide thorough guidelines for the implementation of crowdshipping platforms.

A Robust Mixed-Integer Linear Programming Model for Mitigating Rail Transit Disruptions Under Uncertainty

In this paper, we propose a two-stage robust optimization framework to evaluate widely controllable commuter flow strategies that can help mitigate uncertain disruption impacts on rail transit commuter flows. In the operation-as-usual stage, preemptive control strategies including timetable adjustment, commuter path choice diversion, and commuter trip starting time diversion are planned within budget and other requirement constraints. In the disruption stage, optimal contingency routing is derived to minimize disruption impacts. To this end, a linear programming model that simulates the commuter movement logic under train service and platform disruption events is developed, which is then integrated in a mixed-integer max-min optimization model to evaluate the worst-case impacts on commuter flows arising from a set of uncertain disruption scenarios. We show that the two-stage robust optimization framework can be solved by a cutting plane algorithm efficiently using high performance and parallel computing platforms. Finally, we demonstrate the application of the model in mitigating disruption impacts through computational studies based on an actual rail transit network.

Urban Commute Travel Distances in Tehran, Istanbul, and Cairo: Weighted Least Square Models

A very large part of the literature on urban commute travels is related to high-income countries. The determinants of urban commute trip distances are not clear in the Middle East and North Africa (MENA); thus, this study attempts to shed light on this topic in relation to Tehran, Istanbul, and Cairo. The objective is to clarify which environmental and human factors are correlated with commuting distance in these cities. Using primary disaggregate data produced by surveys in the three cities (n = 8237) in 2017, weighted least square regressions showed that fifteen significant or highly significant variables, including individual and household characteristics, mobility decisions, residential location, and land use attributes, predict the lengths of urban commute trips in the MENA sample. Unlike western countries, age and gender are not significant predictors of commute distance in MENA large cities. The results of independent-sample Kruskal–Wallis test show that there is a significant difference between the mean one-way commute travel distances in the three cities (Tehran: 9096 m, Istanbul: 10839 m, and Cairo: 6670 m); however, there are some similarities in the determinants of commute distance in the three cities. The results can be adopted to reduce commute trip lengths by providing a more connected street network and accessible neighborhood-level facilities.

The flexible and real-time commute trip sharing problems

The Commute Trip Sharing Problem (CTSP) was introduced to remove parking pressure on cities, as well as corporate and university campuses. Its goal is to reduce the number of vehicles being used for daily commuting activities. Given a set of inbound and outbound requests, which consists of origin and destination pairs and their departure and return times, the CTSP assigns riders and a driver, as well as inbound and outbound routes, to each vehicle in order to satisfy time-window, capacity, and ride-duration constraints. The CTSP guarantees a ride back for each rider, which is a critical aspect of such a ride-sharing system. This paper generalizes the CTSP to account for uncertainties about the return trip. Each rider is assumed to have a return time specified by a distribution (learned from historical data) and, each day, a percentage of riders will want to preprone or postpone their return trip to accommodate some schedule changes. The paper proposes two generalizations of the CTSP: the Flexible CTSP (FCTSP) and the Real-Time CTSP (RT-CTSP). In the FCTSP, riders must confirm their final return times by a fixed deadline. In the RT-CTSP, riders confirm their new return times in real time with some prior notice. The paper proposes a two-step approach to address the FCTSP and the RT-CTSP. The first step uses a scenario-based stochastic program to choose the drivers and the morning routes in order to maximize the robustness of the driver assignment. The second step reoptimizes the plan at the fixed deadline or in real time once the return times are confirmed. Experiments on a real-world dataset of commute trips demonstrate the effectiveness of the algorithm in generating robust plans and reveal a trade-off between vehicle reduction and plan robustness as the robust plans tend to be conservative. A method is then proposed to evaluate this trade-off using the per-unit price ratio of vehicle increase to uncovered riders.

Influence of Community Design and Sociodemographic Characteristics on Teleworking

The traffic on the roads in many countries has been increasing in recent decades, and the increases in congestion and carbon emission are contributing significantly to climate change. To minimize these adverse effects, the use of more sustainable travel modes, such as public transit, walking, bicycling, carpool and ridesharing, has been widely promoted. Apart from these travel modes, alternatives, such as teleworking, which reduces commute trips, should also be promoted. The objective of this study is to identify different neighborhood design and social characteristics that are associated with teleworking. In this case study, a multiple regression model is applied to 2011 census data and road infrastructure data of 185 communities from the city of Calgary in Canada. In addition, a random intercept model is estimated to account for unobserved heterogeneity. We find that different street patterns, geographical size, land use, mass rapid transit, and road types have a significant effect on teleworking or working-at-home and should be considered when designing new communities. We also find several significant sociodemographic characteristics, including family size, marital status, children, housing type and language. Policy implications based on this research are then provided.

Give public transit a chance: A comparative analysis of competitive travel time in public transit modal share

Extensive research has found that people are more likely to choose the transport alternative which offers shorter travel time. But few studies approached the role of travel time across different transport alternatives and cities. This research assesses the influence of competitive travel time between car and public transit in public transit modal share for commute trips. São Paulo, New York, and Tokyo were selected to perform the analysis. A Fractional Logistic Regression in the binary form was drawn, and a competitive travel time index was calculated based on the Car/Public transit travel time ratio weighted by the number of the employed population at the origin and jobs at destinations in the absence of an Origin-Destination matrix. Findings suggest that, though car ownership was identified as the major factor, Car/Public transit travel time ratio is positively associated with the increase of public transit modal share. Furthermore, the Car/Public transit travel time ratio effect in public transit modal share consistently increases as people get increased access to cars.

Do changes in the residential location lead to changes in travel attitudes? A structural equation modeling approach

Numerous studies have found that travel attitudes might not only affect travel behavior, but also the residential location choice as people might choose a residential location based on their travel preferences and needs (i.e. transport-related residential self-selection). However, it might also be possible that the residential location and travel behavior influence attitudes towards travel. In this study—using quasi-longitudinal data—we analyze how a change in the residential environment affects attitudes towards specific modes, both directly and indirectly through changes in mode frequency (of commute and leisure trips). Using a structural equation modeling approach on 1650 recently relocated residents in the city of Ghent, Belgium, this study indicates that moving to a more urban type of neighborhood improves attitudes towards public transport and active travel. Especially for leisure trips the effects from changes in the built environment on attitudes are partly indirect through changes in mode frequency. This study offers new insights into the links between the built environment, travel behavior and attitudes. We provide further evidence that the built environment influences travel attitudes, but also indicate that these effects are partly mediated by travel mode frequency.