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Analysis of Route Choice Decisions by Long-Haul Truck Drivers
Abstract
This study has done an empirical analysis of long-haul truck drivers' route choice decision making as they navigate the U.S. highway network. The most important factor that has been analyzed is how long-haul truck drivers trade off between distance and time when faced with multiple routes. From information gathered from a revealed preference data set consisting of about 250,000 trucks over a 13-day period, a logistic model was constructed to describe route choice behavior when truck drivers are faced with alternate routes. The logistic model predicted the percentage of trucks that used the bypass route as a function of the perceived speed on the downtown route. The results of this study show that time is a significant factor in the decision-making process.
... Because of their possibility of occurring and potentially catastrophic nature, the associated risks turn to shape attitudes and actions of players concerned. The relationship between hazardous material transportation and risks has been theorized to take the form of 'absent' presence or fire space (Law and Mol, 2001). Law and Mol (2001) have argued that within a spatial setting, the risks posed by hazardous material transporting trucks are defined by their presence, their absence as well as a simultaneous absence and presence, hence the absent presence. ...
... The relationship between hazardous material transportation and risks has been theorized to take the form of 'absent' presence or fire space (Law and Mol, 2001). Law and Mol (2001) have argued that within a spatial setting, the risks posed by hazardous material transporting trucks are defined by their presence, their absence as well as a simultaneous absence and presence, hence the absent presence. To them, absent presence could be understood from the analogical point where fire is both present and absent at the same time: thus, in one vein it is visible, puts forth heat and light, and undeniably has and leaves behind material traces of its presence, but in the other vein it is not a stable object that can be pinned down at a specific place at a specific moment. ...
... However, they should be based on UN Model Regulations on the Transport of Dangerous Goods which present a scheme of provisions that allow uniform development of national and international regulations governing the various modes of transport (United Nations, 2015). A number of the well documented regulatory measures include driver training Kara and Verter, 2004); controlling driving hours (West Africa Trade Hub, 2010); container specifications (Berman et al., 2007;Kara and Verter, 2004); inspection stations to monitor compliance (Kara and Verter, 2004); toll policies (Marcotte et al., 2009); vehicle and container designs (Guo and Verma, 2010); and route selection (Erkut and Alp, 2007;Knorring et al., 2005;Kara and Verter, 2004). Despite these interventions, there are considerable threats to humans in the transportation of hazmats, especially in developing countries where compliance to regulations and standards have been far from satisfactory. ...
This study estimates population exposure risks associated with the transportation of hazardous materials using the Accra – Kumasi Highway (N6) in Ghana as a case corridor. Using the mixed methods approach which resulted in collecting both qualitative and quantitative data from transport operators and relevant agencies, the paper estimates that accident probabilities were observed to be high on relatively longer sections of the road (those over 50 km) as there are few speed control measures to check over speeding. It was also found that the risk of a single shipment of a hazardous material was 0.09144 inhabitants per vehicle/km, implying approximately 1464 inhabitants stand a risk of either being killed or injured over the entire 293.3 km stretch of the N6. The paper recommends the use of multiple risk prevention measures by all drivers as it has been shown in the study results that truck operators who combined at least three measures were those who had not been involved in accidents in the last ten years.
... Thus, a haulier will select a potentially longer, but more efficient route, if lower vehicle operating costs are available. Knorring et al. (2005). ...
... Income Knorring et al. (2005). ...
... Hazards avoided Knorring et al. (2005). ...
... Only few behavioural studies on route choice decision-making of truck drivers can be found in the existing literature. Empirical studies by Kawamura (2002), Knorring et al. (2005) and Vadali et al. (2009 consider the trade-off behaviour of truck drivers among distances, times and/or toll costs when faced with multiple routes. Road accessibility factors have not received much attention in quantitative research to date. ...
... Only few behavioural studies on route choice decision-making of truck drivers can be found in the existing literature. Empirical studies by Kawamura (2002), Knorring et al. (2005) and Vadali et al. (2009 consider the trade-off behaviour of truck drivers among distances, times and/or toll costs when faced with multiple routes. Road accessibility factors have not received much attention in quantitative research to date. ...
In this paper, we report the results of a stated choice experiment, which was conducted to examine truck drivers’ route choice behavior. Of particular interest are the questions (i) what is the relative importance of road accessibility considerations via-a-vis traditional factors influencing route choice behavior, (ii) what are the influences of particular personal and situational variables on the evaluation of route attributes, (iii) how sensitive are truck drivers for possible pricing policies, and (iv) is there a difference in impact if environmental concerns are framed as a bonus or as a pricing instrument. The main findings indicate that road accessibility characteristics have a substantial impact on route preferences which is of the same order of magnitude as variation in travel times. This suggests that provision of adequate travel information in itself can be an effective instrument to prevent negative externalities of good transport associated with shortest routes. Furthermore, the results indicate that truck drivers/route planners when choosing a route are relatively sensitive to road pricing schemes and rather insensitive to environmental bonuses.
... The raw GPS traces were map-matched to a high-resolution transportation network to derive more than 225,000 truck trips and their routes for use in this analysis. Given that the majority of route choice studies, other than a few exceptions (Arentze et al., 2012;Feng et al., 2013;Hess et al., 2015;Knorring et al., 2005), are in the context of passenger car or bicycle route choice, this study contributes to a currently small body of literature on generating route choice sets for modeling freight truck route choice. ...
The primary goal of this research was to use large streams of truck-GPS data to analyze travel routes (or paths)
chosen by freight trucks to travel between different origin and destination (OD) location pairs in metropolitan
regions of Florida. Two specific objectives were pursued. The first objective was to measure and analyze the
diversity in travel paths chosen by trucks between different OD locations in Florida. Various metrics were used to
measure three different dimensions of diversity in truck route choice between any given OD pair. Further, statistical
models of diversity metrics were estimated to gain insights on the determinants of various dimensions of truck route
choice diversity between an OD pair. The second objective was to evaluate truck route choice set generation
algorithms and derive guidance on using these algorithms for effective generation of choice sets for modeling truck
route choice. Specifically, route choice sets generated from a breadth first search link elimination (BFS-LE)
algorithm were evaluated against observed truck routes derived from large streams of GPS traces of a sizeable
truck fleet in the Tampa Bay region of Florida. A systematic evaluation approach based on the algorithm’s ability
to generate relevant routes typically considered by travelers and generation of irrelevant (or extraneous) routes
seldom chosen is presented. Based on this evaluation, the study offers guidance on effectively using the BFS-LE
approach to maximize the generation of relevant truck routes while eliminating irrelevant routes. It was found that
carefully-chosen spatial aggregation can reduce the need to generate a large number of routes for each trip.
Finally, route choice models were estimated and applied on validation datasets to corroborate the findings.
... The odds ratio showed that the expected change in the 41 odds of selecting the north route increased 1 percent when the north route became less reliable. 42 As shown in Table 2, the correlation between TTIRATIO and PTIRATIO was 0.52. Even this is 43 not a very large correlation value; with a large dataset and a binary response, the correlation 44 between these two variables could lead to questionable results. ...
This study represents the first research to investigate the impacts of two critical determinants—level of congestion and travel time reliability—on routing decisions with two groups of truck drivers having different levels of awareness of the real-time and the historical traffic conditions on available routes. The research analyzed 14,538 global positioning system devices recording trips on the I-495 crossing through Maryland, Virginia, and Washington, DC, and 2,166 trips in the Dallas area, to explore how truck drivers make routing decisions based on real-time travel time and reliability information by applying a binary logistic regression model. Researchers found that for truck drivers who are not familiar with the historical traffic and travel time conditions on available routes, real-time congestion information is a significant factor in their routing decision-making process, while travel time reliability is not a major consideration. For frequent truck drivers who are familiar with the historical traffic and travel time conditions on available routes, travel time reliability is a significant factor in their routing decision-making process, and traffic congestion information is not a significant factor. These results bring more accuracy to travel time prediction and provide valuable insights into traffic management and reliability performance measures. Moreover, this research provides statistical evidence proving the potential value of delivering travel time reliability information to drivers, traffic management agencies, and navigation map developers.
... When accounting for the schedule delay, the travel time itself was not significant in predicting route choices. Knorring et al. (2005) found that long-haul truckers are willing to trade an increase of 1% in their travel distance for a speed gain of 0.4 mph in situations in which they have a choice between a route passing through a metropolitan area and a bypass route. Hyodo and Hagino (2010) found an effect for the road type, in addition to tolls and travel times, on truck route choices in Japan. ...
... Hagino et al. (2010) used records of traffic permit applications submitted by drivers. Knorring et al. (2005) collected GPS traces using in-truck systems. However, both studies suffer from substantial limitations: they do not collect any information about the shipments or the drivers. ...
This paper reports on tools, methods and experimental designs that have been developed to study the routing behavior and movement of trucks. The application of these capabilities is demonstrated with a case study on the route choices of North American intercity truck drivers’, with a focus on the choice between tolled and free roads. An extension to the urban freight context, currently ongoing in Singapore, is briefly discussed, highlighting the challenges and main differences compared to the intercity case.
... ata and photos respectively as the possible predicting route. Various aspects have also been considered in travel behavior analysis. From the research of Li et al. (2013), route choice behavior changes relating to the familiarity to OD pairs while Lanser et al. (2005) focused on overlap in multimodal transport networks using the path size modeling. Knorring et al. (2005) had an empirical analysis of long-haul truck drivers, with the result showing that time is a significant factor in the decision-making process and Spissu et al. (2011) proved that higher levels of intra-individual variability were found for discretionary trips, whereas higher levels of inter-individual variability, as well as greater de ...
The lack of sufficient data is the result of the inherent complexity of gathering and subsequently analyzing route choice behavior, which unfortunately hasn't been revealed much by existing literatures. With the assistance of GIS technology and taxi-based floating car data, the authors found that the majority of urban drivers would not travel along the shortest or the fastest paths. This paper studies the factors that influence commuters' route choice and route switching based on objective real-world observations of travel behavior. Possible factors that may affect driver's route choice are then analyzed and regression methods were introduced to attain if there existing a clear quantitative relationship between drivers' route choice and these factors. The result indicates that such connection is difficult to be established. Consequently, eight scenarios were proposed to quantify the influence of various potential factors. Analysis shows that travel distance, travel time and road preference have comparable higher influence on drivers' route choice. To this end, a new route prediction model is proposed, adopting the road usage as the weight and the shortest route's length and the fastest route's time as the constraints. The proposed model was implemented and validated using the FCD data of Shenzhen, China. The results indicate that by combining the external influence with the driver's personal preference, the predicted travel route has a higher matching ratio with the actual one, which consequently indicates the effectiveness of the model.
... First, this study adds distance into the measure of school competition. The distance between competitors is often ignored in the traditional market structure theory, but spatial proximity plays an important role in the human decision-making process [22]. Thus, it is important to consider distance when determining market competition. ...
This research develops a gravity-based index of public school competition from private schools within local markets. Proponents of educational reform often call for policies to increase competition between schools. A major hurdle for researchers examining this issue is to determine a workable definition of “competition” by which they can measure the degree of competition within local markets. This study addresses this challenge by developing a school competition index for public schools in the Jackson metropolitan area of Mississippi, USA that considers the enrollments in public schools and the enrollments in their neighboring private schools, as well as the distances between them. The school competition index reveals the degree of competition for each public school based on its spatial location relative to peer private schools operating within its service area. This methodology can be useful for evaluating competition in other markets and redefining the traditional market structure.
This research presents the data collection, specification and estimation of a route choice model for intercity truck trips, with a focus on toll road usage. The data was obtained from driver-validated and enhanced GPS records. A mixed logit model with a path-size factor is specified. It accounts for heterogeneity among drivers using distributed coefficients for travel time and its variability. The estimation results show wide heterogeneity among drivers based on employment type and availability of electronic toll collection tags. Toll value of time and toll value of reliability distributions are derived. The model application is demonstrated on several trip corridors.
This thesis evaluates truck route choice set generation algorithms and derives guidance on using the algorithms for effective generation of choice sets for modeling truck route choice. Specifically, route choice sets generated from a breadth first search link elimination (BFS-LE) algorithm are evaluated against observed truck routes derived from large streams of GPS traces of a sizeable truck fleet in the Tampa Bay region of Florida. A systematic evaluation approach is presented to arrive at an appropriate combination of spatial aggregation and minimum number of trips to be observed between each origin-destination (OD) location for evaluating algorithm-generated choice sets. The evaluation is based on both the ability to generate relevant routes that are typically considered by the travelers and the generation of irrelevant (or extraneous) routes that are seldom chosen. Based on this evaluation, the thesis offers guidance on effectively using the BFS-LE approach to maximize the generation of relevant routes. It is found that carefully chosen spatial aggregation can reduce the need to generate large number of routes for each trip. Further, estimation of route choice models and their subsequent application on validation datasets revealed that the benefits of spatial aggregation might be harnessed better if irrelevant routes are eliminated from the choice sets. Lastly, a comparison of route attributes of the relevant and irrelevant routes shed light on presence of systematic differences in route characteristics of the relevant and irrelevant routes.
Understanding truck drivers’ routing selection behavior according to congestion level, travel time reliability, and other factors can not only help transportation agencies improve the efficiency of traffic management but also increase the accuracy of travel time predictions. However, most of the existing studies on this subject used nonempirical methods such as stated preference, experimental, and theoretical modeling and simulations because real field data were not available. This research analyzes 17,024 observed trips on I-495 crossing through Maryland, Virginia, and Washington, D.C., to explore how truck drivers make routing decisions based on real-time congestion information, travel time reliability, and other factors such as rush hour and day of the week. The east loop of I-495 is defined as the east route, and the north loop is defined as the north route. The results show that the odds of selecting the north route significantly decrease if the travel time index ratio between the north route and east route increases. The research also demonstrates that the planning time index ratio has a significant impact on routing selection. Also, freight drivers’ routing decisions are influenced differently by factors such as morning rush hour, afternoon rush hour, and whether it is a weekday or weekend. A similarly detailed aggregate freight dataset from the Dallas–Fort Worth area validates the results from the Maryland dataset.
Mobilfunkgeräte sind in den letzten Jahren zum ständigen Begleiter der meisten Menschen in Deutschland und in der ganzen Welt geworden. Das Mobilfunknetz kennt während des normalen Betriebs für jedes angeschaltete Mobilfunkgerät die aktuellem ungefähr 30 bis 40 Funkzellen umfassende Location Area. Damit bieten sich die im Betrieb anfallenden Mobilfunkdaten als Datenquelle für Verkehrsingenieure an, um eine große Anzahl von Ortsveränderungen kontinuierlich zu beobachten.
Der erste Teil dieser Arbeit beschreibt ein Verfahren, mit dem aus Mobilfunkdaten Trajektorien von Mobilfunkteilnehmern generiert werden können. Diese Methode basiert auf den Daten von Location Area Updates, die immer dann erfolgen, wenn ein Mobilfunkgerät eine Location Area verlässt und in eine andere Location Area wechselt. Diese finden auch dann statt, wenn keine Telefonate geführt werden. Somit können mit dieser Methode die Ortsver¬änderungen aller einge¬schalteten Mobilfunkgeräte im untersuchten Mobilfunknetz erfasst werden.
Die Durchführung des Verfahrens mit Mobilfunkdaten von T-Mobile im Untersuchungs-gebiet im Nord-Westen Baden-Württembergs ergibt, dass Trajektorien ab einer Länge von ungefähr 20 Kilometern erzeugt werden können, wobei die Qualität der Trajektorien mit ihrer Länge zunimmt. Damit eignet sich das Verfahren vor allem für Autobahnen und ausgewählte Bundesstraßen, nicht aber für innerstädtischen Verkehr.
Im zweiten Teil der Arbeit werden die Trajektorien aus Mobilfunkdaten dazu verwendet, das Routenwahlverhalten mit Maximum-Likelihood-Schätzungen zu analysieren. Dabei wird untersucht, welche Einflussgrößen Verkehrsteilnehmer bei der Routenwahl berücksichtigen. Mögliche Einflussgrößen sind dabei die Routenempfehlungen auf dynamischen Anzeigen entlang der Straße sowie Verkehrs¬meldungen, die über den Verkehrsfunk bzw. das Navigationsgerät empfangen werden.
Die Ergebnisse in der Autobahn-Netzmasche zwischen Stuttgart, Karlsruhe, Walldorf und Heilbronn unter Nutzung von ungefähr 1 Million Trajektorien zeigen deutlich, dass die Verkehrsteilnehmer beide genannten Einflussgrößen bei ihren Routenwahlent-scheidungen berücksichtigen. Ungefähr 30 % der Verkehrsteilnehmer auf der Standardroute wechseln bei einer entsprechenden Routenempfehlung auf die Alternativroute. Eine ähnliche Wirkung haben akkumulierte Verkehrs¬meldungen von sieben Kilometer Stau auf der Standardroute.
Für den Bereich der Netzbeeinflussungsanlage im Norden von Stuttgart, die den Verkehr zwischen der Autobahn A81 und Stuttgart auf verschiedene Routen steuert, lässt sich dagegen keine Wirkung der Verkehrsmeldungen nachweisen. Zudem ist der Befolgungsgrad der innerhalb des Untersuchungszeitraums von drei Monaten nur selten aktiven Routenempfehlungen auf dynamischen Anzeigen mit 3 % stadtauswärts bzw. 17 % stadteinwärts geringer als in der BAB-Netzmasche.
Somit zeigen die Ergebnisse dieser Arbeit, dass Mobilfunkdaten eine sehr interessante Datenquelle für Verkehrsingenieure sind.
This paper uses freight performance measure (FPM) data to assess quantitatively the local effects of an Interstate closure. The use of FPM data allows for an empirical analysis beyond traditional theoretical modeling, simulations, user surveys, windshield surveys, and manual traffic counts. Specifically, this paper presents a case study of the localized changes in regional freight flows with origins and destinations in Kansas City, Missouri, and Kansas City, Kansas, and in Omaha, Nebraska, and Council Bluffs, Iowa, during the Missouri River flooding of 2011, which caused 49 mi of Interstate 29 to be closed for 115 days. Analysis results show that most of the regional truck traffic did not adhere to the detour routes designated by the state department of transportation, which aimed to keep the trucks on Interstates or four-lane divided facilities. Other routes used were found to be advantageous for driver costs, vehicle operating costs, travel time, and vehicle miles traveled. On average, as a result of the closure, vehicle and driver costs increased by an estimated 10.1%, travel time increased by 21%, and vehicle miles traveled increased by 12%. Finally, analysis of the progression of route selection during the 4-month period of the Interstate closure concluded that regional truck route selection progressed during the 4 months to the most efficient alternate routes between Kansas City and Omaha-Council Bluffs.
This research studies the decision-making process and factors that affect truck routing. The data collection involved intercept interviews with truck drivers at three rest area and truck stop locations along major highways in Texas and Indiana in the United States and Ontario, Canada. The computerized survey solicited information on truck-routing decisions, identity of the decision makers, factors that affect routing, and sources of information consulted in making these decisions. In addition, stated preferences (SP) experiments were conducted, in which drivers were asked to choose between two route alternatives. A total of 252 drivers completed the survey, yielding 1,121 valid SP observations. These data were used to study the identity of routing decision makers for various driver segments and the sources of information used in pretrip planning and en route. A random-effects logit model was estimated with the SP data. Results show that there are significant differences in the route choice decision-making process in the various driver segments, and that these decisions are affected by multiple factors beyond travel time and cost. These factors include shipping and driver employment terms, such as the method of calculating pay and the bearing of fuel costs and tolls.
This article identifies the truck routing priorities of freight companies through a survey of Washington state shippers, carriers, and receivers. To elicit these priorities, the survey prompted the respondents to rate 15 items believed to affect route choice decision making with respect to each item's influence on route choice. Item response theory (IRT) and latent class analysis (LCA) highlights priorities that were common among all survey respondents and priorities that were different among the sample. Minimizing cost and meeting customer requirements were priorities for all. The influence of other items such as road grade, hours of service limits, and driver availability depended on whether the respondent was best described as a long-haul, local-regional, or urban trucking provider. These three classes of companies were derived from the LCA, and each class has a distinct response pattern to the 15 routing items. This result suggests that truck routing priorities are not constant and uniform across a state's trucking industry but rather variable and largely dependent on trip length. The paper concludes with practical recommendations as to how these priorities can be implemented within a truck routing model.
The use of in-car navigation systems has rapidly grown over the last years. For freight transport, the emphasis on fastest routes is not always in the interest of the truck driver and causes negative externalities when the routes run through built-up area. This paper reports the results of a field test that was conducted to test a new navigation system for trucks. A sample of 100 truck drivers participated in the experiment where they used the new system first in a tracking mode only and next in a full navigation mode for a period of two and a half months in total. During this period drivers kept a diary where they indicated the times and reasons why they deviated from routes suggested by the system. We analyze the diary data and GPS data logged by the system to investigate compliance with and influence of the new navigation system on route choice. The results suggest that the new navigation system for trucks has significant impacts on routes, the most important of which is a shift from use of smaller roads towards larger roads with beneficial consequences in terms of both efficiency and environment. Deviation from suggested routes occurs relatively frequently and for different reasons by drivers of heavy and lighter trucks. We conclude that development of dedicated navigation systems for trucks would serve the interest of the freight transport sector and quality of environment at the same time and more so when route advice is differentiated between heavy and lighter transport.
Direct user fees based options are gaining further momentum all across the United States and particularly in the state of TX. The success of such ventures or projects requires a clear assessment of demand for toll roads among the potential user groups. However, there is too little information about the trucking industry as far as their attitude towards toll roads is concerned. This lack of attention to response patterns can lead to optimism bias in truck toll forecasts. Through literature reviews, Texas specific focus groups, and surveys this study aims to establish the range in demand variation and route preferences for tolled roads across various segments of the trucking community. Fuel prices are found to influence route choices and consequently toll road revenue forecasts. In addition to fuel costs, the other trade-offs that emanate from this study include cargo characteristics, haul characteristics, etc. Therefore a better understanding of the demand structure of the trucking firms requires all the relevant trade-offs be taken into consideration.
Optimism bias is a consistent feature associated with truck toll forecasts, à la Standard & Poor’s and the NCHRP synthesis reports. Given the persistent problem, two major sources of this bias are explored. In particular, the ignorance of operating cost as a demand-side factor and lack of attention to user heterogeneity are found to contribute to this bias. To address it, stochastic dominance analysis is used to assess the risk associated with toll revenue forecasts. For a hypothetical corridor, it is shown that ignorance of operating cost savings can lead to upward bias in the threshold value of time distribution. Furthermore, dominance analysis demonstrates that there is greater risk associated with the revenue forecast when demand heterogeneity is factored in. The approach presented can be generally applied to all toll forecasts and is not restricted to trucks.
Integrated freeway corridor control, which includes efficient real-time management of freeway traffic diversion onto less congested arterials, is one of the most cost-effective ways to cope with freeway congestion. Because traffic diversion is influenced by ramp metering and intersection signal timing, the effectiveness of an integrated corridor control strategy draws on its ability to predict the diversion resulting from the control in real time. An adaptive demand-diversion predictor is developed that reflects the drivers' choice behavior in a rapidly changing traffic environment. The new method explicitly treats the time-variant effects of control on the traffic demand to be predicted by combining behavioral modeling with filtering. Behavioral demand-diversion models and an extended Kalman filter are developed, with the filter continuously updating the model parameters with the most recent prediction error. The method was applied in several freeway entrance ramps of the Minneapolis-St. Paul metropolitan area freeway system to predict the demand-diversion of traffic flow approaching the ramp area in real time. Following extensive testing and evaluation, the method was incorporated in a new demand-responsive control logic for the online control of freeway corridors.
We review the case against the standard model of rational behavior and discuss the consequences of various anomalies of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.
An analysis of commuters' choices of routes and departure times is undertaken using a discrete/continuous econometric modelling structure. The modelling system is estimated with morning work trip data collected in State College, Pennsylvania. The estimation results provide interesting insights into the behavioral aspects motivating route and departure time choices and underscore the need for proper econometric specification in discrete/continuous model structures. Overall, the model estimations provide surprisingly good fits and show promise for applications in a traditional user equilibrium framework.