Origin-destination Pairs Research Articles

A cumulative gravity model for inter-urban spatial interaction at different scales

The classical gravity model assumes the knowledge of origin-destination (OD) flows between places. While this is reasonable for some modes of transportation such as air travel, it presents challenges to many other applications such as road travel where the observed flow on road segments, such as annual average daily traffic (AADT), may involve traffic for multiple OD pairs. This article introduces a methodology to calibrate a cumulative gravity model on a scale-specific road network. The model infers the traffic on OD pairs from the knowledge of link flows and we document its effectiveness in predicting the bidirectional volume of traffic on highways in the United States. Census data and state AADT were used to calibrate both the noncumulative and cumulative gravity models in three different regions in the western US: Arizona only, 10 western US states, and 16 western US states. The road networks were built by defining nodes as population centers of >10,000 inhabitants for Arizona and >500,000 for the other two networks. A systematic method to remove junction nodes that do not satisfy population threshold requirements but are necessary to maintain network connectivity is presented. The cumulative gravity model performed better (with a R-squared value of 0.93 in Arizona) than the standard model, but the improvement, based on two goodness-of-fit metrics (Common Part of Commuters and least squares) was above 2% only in the Arizona network. Removal of commercial traffic from the data further improved the model's calibration in the 10-state network. A thresholding method that connects the cumulative to the standard gravity model reveals that most road trips in Arizona are within the 150-mile range and that this distance increases to 500 miles in both western networks. Potential future applications of the present work are discussed.

Neighborhood socio-demographic characteristics and bike share member patterns of use

Information about factors that affect patterns of use by bike share members is needed to manage systems effectively and equitably. Recent research into the patterns of use by members across heterogeneous neighborhoods indicates that neighborhood characteristics as well as built environment characteristics may be associated with use. We use 2017 data on trips taken by 30-day and annual members of the Nice Ride Bike Share System in Minneapolis-St. Paul and examine the associations between user behavior and socio-demographic characteristics at the Census block group (CBG) level where members live. We estimate linear mixed-effects models and multinomial logistic models to analyze the associations of neighborhood socio-demographic characteristics with, respectively, (1) the frequency and duration of weekday and weekend trips by members and (2) the temporal and spatial patterns of their trips. Our results show that, after controlling for station accessibility, nearby bike infrastructure, the built environment, gender, and age, members who reside in minority-concentrated and lower socio-economic status (SES) neighborhoods use bike share more frequently, take trips at more varied times-of-day and across days-of-week, and have more frequently-used origin-destination pairs of stations. A limitation of our analysis is that patterns of use of casual users are not investigated. Our findings have implications for efforts to serve members in neighborhoods with higher concentrations of minorities and residents of lower SES and illustrate the need for more detailed surveys of members to obtain additional information about individual characteristics associated with behaviors of bike share users.

Modeling the Effect of New Commuter Bus Service on Demand and Impact on Greenhouse Gas Emissions: Application to Greater Boston

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas. Any efforts to reduce these emissions require strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and providing incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region where modes include driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit, and bus) by walk-access or drive-access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board, the net effect of a new commuter bus service on GHG emissions from transportation is estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of new commuter bus services for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

A web service based advanced traveller information system for itinerary planning in an uncertain multimodal network

This article intends to provide an advanced traveller information system based on open geospatial consortium (OGC) standards to perform a fuzzy bi-criteria shortest-path analysis for a multimodal transportation network. To do so, chaining of OGC web services is used to resolve interoperability problems among distributed and heterogeneous resource data. First, a web feature service is assigned to each mode of transportation to gather and convert the data to a standard format, such as geographic markup language. Then, the obtained data are combined to model and construct the multimodal transportation network. Subsequently, a web processing service is designed for performing a fuzzy bi-criteria shortest-path analysis in the multimodal transportation network. Finally, the best routes between the origin–destination pair are presented on a digital map by using a web map service. In the study, among various route-selection criteria, travel time and comfort are considered. To model these criteria, the fuzzy set theory is utilized. By using fuzzy decision tables and a Tsukamoto fuzzy inference system, the desirability of the routes is calculated. The results of comparison among the four travel routes between various origin–destination pairs indicate that the proposed system could be an applicable tool for providing pre-trip information and assisting multimodal travellers under uncertain conditions.

Multi-Armed Bandit On-Time Arrival Algorithms for Sequential Reliable Route Selection under Uncertainty

Traditionally vehicles act only as servers in transporting passengers and goods. With increasing sensor equipment in vehicles, including automated vehicles, there is a need to test algorithms that consider the dual role of vehicles as both servers and sensors. The paper formulates a sequential route selection problem as a shortest path problem with on-time arrival reliability under a multi-armed bandit setting, a type of reinforcement learning model. A decision-maker has to make a finite set of decisions sequentially on departure time and path between a fixed origin-destination pair such that on-time reliability is maximized while travel time is minimized. The upper confidence bound algorithm is extended to handle this problem. Several tests are conducted. First, simulated data successfully verifies the method, then a real-data scenario is constructed of a hotel shuttle service from midtown Manhattan in New York City providing hourly access to John F. Kennedy International Airport. Results suggest that route selection with multi-armed bandit learning algorithms can be effective but neglecting passenger scheduling constraints can have negative effects on on-time arrival reliability by as much as 4.8% and combined reliability and travel time by 66.1%.

A data driven method for OD matrix estimation

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method.

Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

Exploring trip characteristics of bike-sharing system uses: Effects of land-use patterns and pricing scheme change

Abstract Facing the rapidly growing popularity of bike-sharing systems worldwide, this study explores the trip characteristics of using public bikes at the level of zonal Origin-Destination (O-D) pairs, particularly with respect to the effect of pricing scheme change. Based on the transaction records of Youbike, a bike-sharing system in Taipei, Taiwan, which contain data before and after an increase of the rental fee (as the cancellation of a free-usage period for the first 30-minute), the associated demand-side responses are studied in the context of multivariate analysis. Two regression models are developed thereupon to investigate the effect of the change on the O-D demand associated various land-use patterns. A multiple linear regression model is developed for direct before-after analysis on the variation of zonal O-D demand. A finite mixture model is further constructed, which identifies three usage groups with different levels of sensitivity toward the price change. Both models suggest that the pricing scheme change have greater impact on short-distance trips and O-D pairs where alternative transit services are provided; these affected trips can be generally related to frequent users. Such research findings enable better understanding of the usage pattern of bike-sharing systems, which can benefit the associated planning and operation. They also imply the trade-off between the perspective of encouraging greener mobility and other managerial principles regarding public transportation and government subsidy.

Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data

Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the applicability of BLOS rating schemes for the estimation of whole-journey route choice. The methods evaluated share a common trait of being applicable for mixed traffic urban environments: Bicycle Compatibility Index (BCI), Bicycle Stress Level (BSL), Sixth Edition Highway Capacity Manual (HCM6), and Level of Traffic Stress (LTS). Routes are generated based on BLOS-weighted networks and the suitability of these routes is determined by finding the percentage overlap with empirical route choices. The results show that BCI provides the best match with empirical route data in all five origin–destination pairs, followed by HCM6. BSL and LTS which are not empirically founded have a lower match rate, although the differences between the four methods are relatively small. By iterating the detour rate that cyclists are assumed to be willing to make, it is found that the best match with modelled BLOS routes is achieved between 15 and 21% additional length. This falls within the range suggested by existing empirical research on willingness to deviate from the shortest path, however, it is uncertain whether the method will deliver the comparable findings in other cycling environments.

Network Capacity Maximization Using Route Choice and Signal Control With Multiple OD Pairs

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.

Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events.

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower-level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower-level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin-destination pair, eventually converging to the user equilibrium traffic assignment. The upper-level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near-optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.

Data-driven model for passenger route choice in urban metro network

Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.

Network partitioning on time-dependent origin-destination electronic trace data

In this study, we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. These OD pairs consist of electronic traces collected from smartphone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighbourhood level. By means of community detection, we examine the structure of this empirical data set in terms of connectedness. We show that we can distinguish spatially connected regions when we use a performance metric called modularity and the trip directionality is incorporated. From this, we proceed to analyse variations in the partitions that arise due to the non-optimal greedy optimisation method. We use a method known as ensemble learning to combine these variations by means of the overlap in community partitions. Ultimately, the combined partition leads to a more consistent result when evaluated again, compared to the individual partitions. Analysis of the partitions gives insights with respect to connectivity and spatial travel patterns, thereby supporting policy makers in their decisions for future infra structural adjustments.

Assessing network vulnerability using shortest path network problems

This paper identifies and evaluates critical components of a flow-based network from the perspective of the shortest path problem. Two network optimization models are proposed: the first model, called the shortest path network model (SPN), is an extended version of a single shortest path problem to apply for all origin-destination pairs in a network while considering passenger flows; the assessing nodal disruption in shortest paths network model (AND-SPN), which is based on the SPN, assesses the influence of r nodes’ disruption of the network flow pattern. In this paper, network performance is assessed not only by the transport cost as an objective function but also through supplementary criticality indicators reflecting average arc use, average s-t pair cost, and average arc flow. In the case study of the Amtrak rail network, the criticality of stations is evaluated in terms of objective function and of criticality indicators—highlighting the effect of the disruption and allowing comprehensive exploration of it. Aided by the provided list of criticality rankings, decision makers can decide which stations are the most important to protect during special events or disasters or when seeking to enhance transportation network security.

Low-stress bicycling connectivity: Assessment of the network build-out in Edmonton, Canada

Studies have shown that a network of safe, connected, and direct facilities can increase urban cycling levels. During summer 2017, the city of Edmonton, Canada, constructed nearly 20 km of protected bicycle lanes on its core neighborhood streets. A rapid and coordinated network implementation was preferred to the more traditional incremental approach to bike lane construction. In this paper, we evaluate the low-stress connectivity improvements afforded by this network build-out. We first classify streets and trails according to the Level of Traffic Stress (LTS) framework, which we adapt to the metric system. Using only LTS 2 and LTS 1 network links, posited to be adequate for most adults, we apply three analyses. First, we draw “bikeshed” maps, which show areas of connectivity around seven central destinations. Our comparison before and after the build-out points to a better integration of the network, with previously separate bikesheds overlapping and allowing uninterrupted low-stress travel to more destinations. This analysis also allows us to identify several central neighborhoods which are disconnected due to remaining high-stress links. Second, we generate roughly three-hundred hypothetical origins located in the central neighborhoods of the city. Reflecting the improved bikeshed integration, we observe a four-fold increase in connected origin-destination pairs. Finally, we find small reductions in trip lengths between connected pairs for some of the trips that were possible before the build-out. However, important detours are still necessary to remain on an exclusively low-stress network when compared to the shortest path using the full network, regardless of LTS. The primary contribution of our research is to develop a method of analysis, based on straightforward tools, to study the city-wide impacts of targeted infrastructure improvements and is most relevant for cities in the initial stages of bicycle network development.

Obtaining and Modeling Variability in Travel Times From Off-Site Satellite Clinics to Hospitals and Surgery Centers for Surgeons and Proceduralists Seeing Office Patients in the Morning and Performing a To-Follow List of Cases in the Afternoon.

Hospitals achieve growth in surgical caseload primarily from the additive contribution of many surgeons with low caseloads. Such surgeons often see clinic patients in the morning then travel to a facility to do 1 or 2 scheduled afternoon cases. Uncertainty in travel time is a factor that might need to be considered when scheduling the cases of to-follow surgeons. However, this has not been studied. We evaluated variability in travel times within a city with high traffic density. We used the Google Distance Matrix application programming interface to prospectively determine driving times incorporating current traffic conditions at 5-minute intervals between 9:00 AM and 4:55 PM during the first 4 months of 2018 between 4 pairs of clinics and hospitals in the University of Miami health system. Travel time distributions were modeled using lognormal and Burr distributions and compared using the absolute and signed differences for the median and the 0.9 quantile. Differences were evaluated using 2-sided, 1-group t tests and Wilcoxon signed-rank tests. We considered 5-minute signed differences between the distributions as managerially relevant. For the 80 studied combinations of origin-to-destination pairs (N = 4), day of week (N = 5), and the hour of departure between 10:00 AM and 1:55 PM (N = 4), the maximum difference between the median and 0.9 quantile travel time was 8.1 minutes. This contrasts with the previously published corresponding difference between the median and the 0.9 quantile of 74 minutes for case duration. Travel times were well fit by Burr and lognormal distributions (all 160 differences of medians and of 0.9 quantiles <5 minutes; P < .001). For each of the 4 origin-destination pairs, travel times at 12:00 PM were a reasonable approximation to travel times between the hours of 10:00 AM and 1:55 PM during all weekdays. During mid-day, when surgeons likely would travel between a clinic and an operating room facility, travel time variability is small compared to case duration prediction variability. Thus, afternoon operating room scheduling should not be restricted because of concern related to unpredictable travel times by surgeons. Providing operating room managers and surgeons with estimated travel times sufficient to allow for a timely arrival on 90% of days may facilitate the scheduling of additional afternoon cases especially at ambulatory facilities with substantial underutilized time.

Application of Machine Learning to Two Large-Sample Household Travel Surveys: A Characterization of Travel Modes

Even in a context of rapidly evolving transportation and information technologies, household travel surveys remain an essential source of information for transportation planning. Moreover, as planning authorities become increasingly concerned with reducing the use of the private car, travelers’ mode choice patterns should be reexamined. In this study, a machine learning algorithm (Random Forest) was employed to characterize the use of eight different travel modes observed in two consecutive household travel surveys undertaken in Montreal, Canada. The analysis incorporated roughly 160,000 observed trips. The Random Forest algorithm was trained on the 2008 survey data and applied to the 2013 survey. The usefulness of the algorithm was evaluated using two numerical representations: the confusion matrix and the importance matrix. The results of this evaluation showed that the Random Forest algorithm could generate a detailed and precise characterization of travel submarkets for four of the most commonly observed modes of travel (auto-drive, public transit, school bus, and walk) using 11 attributes of households, persons, and trips. However, the auto-passenger mode was difficult to characterize because of its dependence on unobserved intra-household interactions. The algorithm also had difficulty identifying users of rarely observed modes (park-and-ride, kiss-and-ride, bicycle), but performed better in this regard than a traditional mode choice model. Finally, traveler’s age and the spatial orientation of origin–destination pairs were found to be decisive factors in the use of the auto-drive mode. This finding, combined with the stability of mode choice patterns observed over 5 years, highlights the difficulty of significantly reducing automobile use.

An energy-efficient reliable path finding algorithm for stochastic road networks with electric vehicles

In this paper, we develop a novel reliable path finding algorithm for a stochastic road network with uncertainty in travel times while both electric vehicle energy and efficiency are simultaneously taken into account. We first propose a bi-objective optimization model to maximize (1) the on-time arrival reliability and (2) energy-efficiency for battery electric vehicles (BEVs) in a path finding problem. The former objective requires finding the reliable shortest path (RSP), which is the path with the minimal effective travel time measured by the sum of the mean travel time and a travel time safety margin for any given origin-destination (OD) pair. Then, we refer to energy-efficiency as the minimum of the electric energy consumption. We discuss the non-additive property of the RSP problem since we also consider the link travel time correlations, whereas the latter objective satisfies the additive criterion. To this end, we illustrate the existence of non-dominated solutions that satisfy both of the two objectives. Furthermore, it is shown that the intersection of two candidate sets – one for the RSPs and the other for paths with minimal energy-consumption - actually contains the optimal solution for the bi-objective optimization problem. The upper and lower bounds of the effective travel time are mathematically deduced and can be used to generate the candidate path set of this bi-objective problem via the K-shortest algorithm. Our proposed algorithm overcomes the infeasibility of traditional path finding algorithms (e.g., the Dijkstra algorithm) for RSPs. Moreover, using two numerical examples, we verify the effectiveness and efficiency of the proposed algorithm. We numerically demonstrate promising potential applications of the proposed algorithm in real-life road traffic networks.

Impact of route choice and period of the day on cyclists' exposure to black carbon in London, Rotterdam and São Paulo

Cyclists are exposed to direct traffic emissions due to their proximity to on-road vehicles. Several studies associate black carbon (BC) exposure with both mortality and morbidity caused by cardiovascular and respiratory diseases. We did a comparative assessment of cyclists' exposure to BC in three cities: London, Rotterdam and São Paulo. We measured personal exposure to BC during the peak and off-peak hours in all three cities using the same instrument. Three origin-destination (O-D) pairs, each with two routes, for a total of six routes, were chosen in each city. The first route of each O-D pair was along busy major roads and the other perceived to be clean passing close to green/blue/quiet areas. This work brings together results from three different Latin American and European cities, with an aim to understand the BC exposure variabilities while cycling during peak and off-peak hours, identify main pollution hotspots resulting in enhanced exposure and associate the measured concentrations with proximity to green areas and waterways. BC concentrations were higher during the morning-peak hours compared with evening-peak hours in Rotterdam and São Paulo. London showed an opposite trend, with higher concentrations during evening hours. In most cases, the cyclists using the alternative route were found to be less exposed to BC in London and São Paulo. In Rotterdam, the differences in absolute concentrations between main and alternate routes were modest. Each city is different but the common features among all were that the exposure is related to route choice, a period of the day and proximity with the mobile sources. These findings have implications in terms of considering the pollutants exposure when establishing new cycle routes.

Analyzing Location-Based Advertising for Vehicle Service Providers Using Effective Resistances

Vehicle service providers can display commercial ads in their vehicles based on passengers' origins and destinations to create a new revenue stream. In this work, we study a vehicle service provider who can generate different ad revenues when displaying ads on different arcs (i.e., origin-destination pairs). The provider needs to ensure the vehicle flow balance at each location, which makes it challenging to analyze the provider's vehicle assignment and pricing decisions for different arcs. For example, the provider's price for its service on an arc depends on the ad revenues on other arcs as well as on the arc in question. To tackle the problem, we show that the traffic network corresponds to an electrical network. When the effective resistance between two locations is small, there are many paths between the two locations and the provider can easily route vehicles between them. We characterize the dependence of an arc's optimal price on any other arc's ad revenue using the effective resistances between these two arcs' origins and destinations. Furthermore, we study the provider's optimal selection of advertisers when it can only display ads for a limited number of advertisers. If each advertiser has one target arc for advertising, the provider should display ads for the advertiser whose target arc has a small effective resistance. We investigate the performance of our advertiser selection strategy based on a real-world dataset.