Morning Commute Research Articles

Parking Pricing in the Morning Commute Problem Considering Human Exposure to Vehicular Emissions

Walking is the final phase of the morning commute, during which commuters are exposed to vehicular emissions. This study proposes a novel analytical model to evaluate how emission exposure affects commuters’ departure time choices and parking behavior. Different from traditional bottleneck models, our model includes a nonlinear term in the generalized cost function to account for emission exposure. The findings reveal that, at user equilibrium, rational commuters seeking to minimize their own generalized costs will park outward, resulting in undesired scenarios in which all walking commuters suffer from emission exposure. However, we show that in a system-optimal scenario, emission exposure can be eliminated if commuters park inward; the schedule delay cost is minimized in such a parking order. To achieve this outcome, we propose a new spatiotemporal parking pricing scheme designed to reduce the overall system cost and incentivize inward parking patterns. Case studies using empirical data show that this pricing approach, independent of specific parking orders, effectively encourages inward parking, thereby minimizing emissions and improving commuter welfare. Hopefully, findings from this research can provide insights to the development of effective roadside parking pricing strategies.

Willingness to use ridesplitting services for home-to-work morning commute in the post-COVID-19 era

Willingness to use ridesplitting services for home-to-work morning commute in the post-COVID-19 era

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees' parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees' trip scheduling, employer's EP investment decision, and commercial parking operator's pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees' departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer's optimal EP investment decision. A competitive game between employer's investment decision and commercial parking operator's parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

Is It Necessarily Better for More Commuters to Share a Vehicle?

Increasing private car ownership has congested urban roads and made parking more difficult, especially during the morning commute. Carpooling offers a new way to address these problems. This paper studies the dynamic departure patterns for both regular and carpooling vehicles with parking space constraints in the morning commute without the carpool lane. The results suggest that as the parking fee of the central cluster increases, the earliest time for the two types of vehicles to enter the central cluster is delayed. The increase in the proportion of regular vehicles delays the earliest time for carpooling vehicles to enter the central cluster. More commuters sharing a vehicle in the morning commute is not necessarily better. Only a reasonable level of carpooling can reduce the peak time and unnecessary time consumption on the road and effectively promote the reduction in parking fees, commuters’ travel costs, and other societal transportation costs. This research gives practical guidance and suggestions on formulating a reasonable parking fee and controlling a reasonable carpooling level.

“School near workplace” or “school near home”: Which one is better for the morning commute with both individual and household travelers in Y-shaped networks?

Household travelers typically need to coordinate their travel decisions in various aspects, such as destination and trip-timing, resulting in distinctive travel patterns compared to individual travelers. This paper investigates the morning commute problem in a “Y-shaped” network featuring “school near workplace” in consideration of both individual and household travelers and further understands the impact of school locations on the morning commute by comparing it with the “school near home” network. We analytically derive all equilibrium cases in the “school near workplace” network and classify them into three traffic patterns. We analyze the welfare effects of the staggering policy, finding that implementing the staggering policy in the “school near workplace” network can achieve a “win–win” scenario for individuals and household travelers in some equilibrium cases. Also, when the capacity ratio of school-constrained bottleneck to common-constrained bottleneck is below a threshold, which one is better (i.e., “school near workplace” or “school near home”) depends on the free-flow travel costs of the two networks. Locating the school “near workplace” is better than “near home” when the difference in the free-flow related costs for children between the two networks is not very large. Furthermore, we analyze the welfare effects of bottleneck expansion in the “school near workplace” network, finding that bottleneck expansion paradox may emerge when expanding the school-constrained bottleneck. Although properly designing the schedule gap can eliminate the paradox, a significant increase in the schedule gap may be needed to escape from the paradoxical cases when the common-constrained bottleneck capacity is slightly larger than the school-constrained bottleneck capacity. Our study sheds light on the importance of school locations in determining the performance of traffic management policies in the morning commute with both individual and household travelers.

Regulated Pandemic Spaces: Spatial Crises in COVID Comics.

Close-reading sequential comics and cartoons such as He Zhu's "Lockdown," Rivi Handler-Spitz's "Morning Commute," Yang Ji's "Quarantine," and Thi Bui, Will Evans, Sarah Mirk, Amanda Pike, and Esther Kaplan's "In/Vulnerable," this article investigates the networked spatial crises that have emerged during COVID-19. As the global pandemic reshaped social, economic, and cultural landscapes, it is crucial to understand the spatial implications of these transformations. By analyzing graphic medical texts, which serve as visual narratives that capture the lived experiences and perceptions of individuals within these crises, the present essay offers a nuanced exploration of the intricate relationships between space, society, and the effects of the pandemic. The article identifies and examines the various spatial crises that have emerged in the COVID era, such as disrupted urban environments, altered social dynamics, spaces of contamination, contraction of space, and the reconfiguration of workspaces. Drawing on theorists like Michael Foucault and Henri Lefebvre, this essay illustrates how these crisis-induced spatial transformations are represented, experienced, and contested. Ultimately, the article not only contributes to a deeper understanding of the complex interplay between the pandemic and space but also addresses the challenges of our evolving world.

Manage morning commute for household travels with parking space constraints

Parking has always been a thorny problem in urban transport systems. Parking availability and parking reservation management may have a significant influence on the travel behavior of household travelers. This paper explores the morning commute for household travels by considering parking space constraint and the parking reservation mechanism in a two-mode transport system. Household travelers depart from home to their children’s school by car or by rail and then go to work alone. For household travelers who drive, some household travelers have reserved parking spaces while other household travelers must compete for public ones on a first-come-first-served (FCFS) basis. When household travelers do not have a parking spot, they use rail transit for their commute. We derive ten equilibrium departure patterns based on the school-work time difference, the number of parking spots and the ratio of reserved parking spots. With these equilibrium departure patterns, we thoroughly discuss the impact of school-work time difference on the total travel cost of auto travelers and parking reservation ratio. It is found that, given the school-work time difference, appropriate parking reservation allocation can minimize the total system travel cost. Then, the system performance, including total system travel cost and total congestion cost, is examined by regulating the parking supply, the parking reservation allocation and the school-work time difference. We found that there is a unique optimal parking supply to minimize the total system travel cost, and appropriate staggering policy can alleviate traffic congestion and thus improves social welfare. Finally, numerical analysis is conducted to verify the findings, and the Pareto frontier for system performance is analyzed. Traffic authorities can select the optimal solution based on the preferences of traffic indicators to develop effective strategies to manage morning commuting of household travels with parking space constraint. The study demonstrates that implementing reasonable staggering policy and efficient parking management can effectively promote efficiency of urban morning commuting systems and reduce social costs for household travelers.

There and back again: The roles of morning- and evening commute recovery experiences for daily resources across the commute-, work-, and home domain

Commuting is a global phenomenon that has primarily been studied in terms of its costs. However, anecdotes and recent theorizing suggest that some employees enjoy their commutes. Is it, thus, possible that commuting can also be beneficial for employees? We integrate the Work–Home Resources model with the Conservation of Resources theory to conceptualize commuting as a source of recovery that facilitates daily resource gain spanning the commute-, work-, and home domain. Specifically, we hypothesize that morning commute recovery experiences (relaxation, mastery and detachment) trigger resource gains in the work domain, manifesting in increased subjective vitality as a manifestation of physical and cognitive energy. Higher levels of subjective vitality in the work domain, in turn, are positively related to work-to-home commute recovery experiences and associated subjective vitality in the home domain. Furthermore, we explore commute duration as a contingency factor of the relationships between commute recovery experiences and subjective vitality at work and home. A diary across ten workdays largely supports our hypothesized model. On days with higher levels of relaxation during the morning commute, employees experience daily resource gains that culminate in increased evening subjective vitality in the home domain through relaxation during the evening commute.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

Alleviating morning commute congestion through school start time optimization: An exhaustive search approach

One of the major causes of road Traffic congestion in urban transportation networks during the morning rush hour can be attributed to the fact that schools start at the same time. In a modern city center, a large portion of commuters need to drop off their children at school before going to work. When the schools start at the same time, commuters pursuing school-related trips enter the network simultaneously, creating a high demand that the network cannot directly accommodate, resulting in performance degradation. To remedy this shortcoming, we propose a novel approach that regulates the start time of schools, anticipating the emergence of congestion during the morning commute. We consider regional Traffic dynamics to capture the movement of vehicles in the urban network through the Macroscopic Fundamental Diagram. The related problem is formulated as a bi-objective mixed integer nonlinear program whose target is to jointly minimize i) the total time spent by all vehicles inside the network and ii) the associated overall delay observed between the initial and the shifted start time of each school located in the urban network. Dealing with such optimization problems is challenging due to their combinatorial and non-convex nature. To this end, we develop an Exhaustive Search Algorithm, which pinpoints the school start time pair that minimizes the total time spent objective metric. We demonstrate that by properly selecting the school start time, we can shift the peak demand to less congested periods and, as a result, alleviate congestion.

Green Vehicles’ Responses to an Expiring Congestion Toll Exemption: Findings from a Natural Experiment in Stockholm, Sweden

Stockholm established time-varying congestion pricing in 2007, and adopted a toll exemption as a temporary incentive for green vehicles (GVs) that ended in 2012. We examine the behavioral effects of phasing out the exemption by studying the change in cordon crossing events for GV morning commuters between May 2012 and May 2013, with a random sample of conventional vehicles (CVs) as control. The results suggest i) a significant drop in the total number of crossings; ii) a slight shift towards later journeys in the morning; and iii) a reduction in the ratio of peak-toll period crossings to other ones.

The effect of morning commutes on emotional exhaustion and task performance: Taking mental effort and cognitive appraisal into account

Prior research has demonstrated the negative effects of commuting on various employee outcomes, mostly attributing these effects to objective characteristics of the commute (e.g., travel time and distance). Yet different employees respond to similar commuting conditions in different ways. We draw from the conservation of resources theory and the transactional theory of stress and coping to examine whether and how cognitive evaluation of the morning commute shapes employees’ responses to the amount of capacity required during the commute. The results, based on survey and diary data collected from 110 employees, suggest that mental effort and cognitive appraisal of the morning commute, individually and jointly are associated with emotional exhaustion upon arrival at work. In addition, the association between mental effort and task performance is fully mediated by emotional exhaustion. We discuss the importance of cognitive commuting appraisal as an aspect of commuting that is potentially malleable to change through training.

Modeling Morning Commute with Different Kinds of Activities Based on Bottleneck Model

Modeling Morning Commute with Different Kinds of Activities Based on Bottleneck Model

Public or private? Optimal organization for incentive-based travel demand management

Incentive-Based Travel Demand Management (IBTDM) strategies have received more attention in recent years as they are more appealing to commuters than penalty-based strategies and thus result in less public aversion. In IBTDM, fully time-variant positive incentives are endowed to the traveling public to encourage off-peak trips. We theoretically demonstrate the effectiveness of IBTDM in managing morning commuters with elastic demand based on Vickrey's bottleneck model. Due to the decentralization of IBTDM, incentive providers are no longer limited to public organizations, as private organizations can also administrate IBTDM programs. Therefore, from the perspective of public and private organizations, maximizing social surplus and private profit can be treated as objective functions. In addition, IBTDM is usually not mandatory but voluntary. Thus, we also considered the constraint of penetration rate. We found that when commuters are relatively sensitive to changes in their actual travel costs, a low penetration rate and a small incentive budget are enough for IBTDM to reach an optimal state (for both public and private administrators), and an excessive budget actually creates negative effects. Compared with public organizations, incentive schemes administrated by private organizations are a more lightweight strategy. However, public organizations are overwhelmingly dominant in the case of high market penetration and low traveler sensitivity. Therefore, one of the most effective modes of operation is to have private organizations implement IBTDM in the early stages of promotion, and then all private-owned businesses would be integrated into public organizations when they reach a certain scale.

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

Equilibrium analysis of morning commuting and parking under spatial capacity allocation in the autonomous vehicle environment

This study analytically investigates the morning commuting and parking patterns of autonomous vehicles (AVs) under different spatial road capacity allocation schemes (i.e., capacity split between inbound and outbound travel directions). Given that self-driving AV might park far away from commuters’ destination, we investigate equilibrium departure/arrival and parking patterns for AVs subject to the spatial road capacity allocation. We also analyse the system optimum traffic pattern for AV morning commute under a given capacity allocation scheme. Furthermore, we examine optimal capacity allocation strategies under user equilibrium and system optimum AV traffic patterns, respectively, which aim to minimise the total system travel cost. Numerical studies are conducted to illustrate the model and analysis. The results reveal the sensitivity of different efficiency metrics with respect to AV parking supply and road capacity allocation schemes, and provide insights into the infrastructure management with future automated transport.

Congestion behavior and tolling strategies in a bottleneck model with exponential scheduling preference

<abstract><p>The bottleneck model has been widely used in the past fifty years to analyze the morning commute. To reduce the complexity of analysis, most previous studies adopted discontinuous scheduling preference (DSP). However, this handling destroys the continuity in departure rate and differentiability in travel time and cumulative departures. This paper considers an exponential scheduling preference (ESP), which supposes the unit schedule delay cost for commuters exponentially changes with time. With this scheduling preference, we analytically derive solutions and economic properties of user equilibrium and social optimum in the bottleneck model. The first-best, time-varying toll and the optimal single-step toll scheme with ESP are also studied. Results indicate that ESP eliminates the discontinuity in departure rate and non-differentiability in travel time and cumulative departures, which makes the process of morning commute smooth. The ignorance of ESP will lead to underestimation in the queueing time and bias in travel behavior analysis and policymaking.</p></abstract>

Deterministic capacity drop and morning commute in traffic corridor with tandem bottlenecks: A new manifestation of capacity expansion paradox

Deterministic capacity drop is a common phenomenon that occurs in traffic bottlenecks, and it has an intricate impact on the evolution of traffic flow in a traffic corridor with tandem bottlenecks. Considering the above facts, this study investigates deterministic capacity drop’s effects on commuter’s departure time choice, the evolution of traffic flow and traffic congestion in the morning commute process in a traffic corridor with tandem bottlenecks. We derive the closed-form solutions of the departure flow rate and arrival flow rate in different bottlenecks. Based on the formulations of these solutions, we analyze the influences of the deterministic capacity drop on commuter’s trip cost and the existence of different cases in the morning commute process in one origin one destination traffic corridor and Y-shape traffic corridor. A new manifestation of the capacity expansion paradox caused or influenced by the deterministic capacity drop is found. The result of this study reveals the essential role of the deterministic capacity drop in the morning commute process, and provides some advice for traffic control.

Managing the Morning Commute Problem with Tradable Credit Schemes under a Fully Autonomous Vehicle Environment

This study investigates the morning commute problem in a single corridor with a bottleneck under a fully automated vehicle environment. By extending Vickrey’s bottleneck model, we formulate the joint decisions on departure time and parking choices of morning commuters who make trade-offs among travel cost, the cost associated with parking, and the cost of tradable credits. To alleviate traffic congestion and improve social welfare, we propose a time-varying tradable credit scheme and integrate it with the morning commute problem. We explore the travel patterns and the optimal design of tradable credit schemes for the morning commute problem with homogeneous and heterogeneous commuters, respectively. For the homogeneous case, we derive the conditions on the tradable credit scheme to ensure the existence of equilibrium. The system-wide travel cost decreases with parking density after tradable credits are incorporated. Additionally, the efficiency of the tradable credits scheme can be improved by increasing the rate of credit charge rate. For the heterogeneous case, we propose an initial distribution strategy and combine it with the optimal tradable credit in order to guarantee social equity. The commuters with a low value of time (VOT) should be allocated more credits and the commuters with high VOT should be charged more credits. For both cases at system optimum, we find that the equilibrium price of tradable credits increases with parking density and decreases with the total amount of tradable credits.