Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.
Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.
This study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one-dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and redistributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc.
This research explored how two states of affect, emotion and mood, would influence driver’s risky driving behavior through risk perception and risk attitude. An experiment and a survey were adopted to test the two paths. In this model, negative affect played an opposite and more powerful role compared to positive affect. Study 1 was an experimental study with four treatment groups. Participants watched one of four video clips (traffic-related negative, traffic-unrelated negative, positive and neutral) and different emotions were induced. Negative emotion significantly elevated drivers’ risk perception but such perception failed to develop an appropriate attitude for drivers. A more favorable risk attitude resulted in increased reports of speeding. Turning from a “point” effect to a similar “period” effect, a survey was carried out in Study 2 to explore the effect of positive and negative mood instead of emotion. Mood states affected drivers’ risky driving behavior through risk perception as well as risk attitude, which was in line with the results of Study 1. The “bad is stronger than good” effect and the two paths in the model were discussed.
After decades of study, the value of travel time remains incompletely understood and ripe for further theoretical and empirical investigation. Research has revealed many regularities and connections between willingness to pay for time savings and other economic factors including time of day choice, aversion to unreliability, labor supply, taxation, activity scheduling, intra-household time allocation, and out-of-office productivity. Some of these connections have been addressed through sophisticated modeling, revealing a plethora of reasons for heterogeneity in value of time rooted in behavior at a micro scale. This paper reviews what we know and what we need to know. A recurrent theme is that the value of time for a particular travel movement depends strongly on very specific factors, and that understanding how these factors work will provide new insights into travel behavior and into more general economic choices.
This research focuses on investment in roads and highways in part because it is the largest component of public infrastructure in the United States. Moreover, the procedures by which federal highway grants are distributed to states help us identify more precisely how transportation spending affects economic activity.
We find that unanticipated increases in highway spending have positive but temporary effects on GSP, both in the short and medium run. The short-run effect is consistent with a traditional Keynesian channel in which output increases because of a rise in aggregate demand, combined with slow-to-adjust prices. In contrast, the positive response of GSP over the medium run is in line with a supply-side effect due to an increase in the economy’s productive capacity.
This week Britian's Prime Minister David Cameron gave a speech on infrastructure. He touched upon many different industries and modes, but this is what he said about highways:
Now, road tolling is one option, but we are only considering this for new, not existing capacity. For example, we’re looking at how improvements to the A14 could be part-funded through tolling. But we now need to be more ambitious. We should be asking ourselves, ‘Why is it that other infrastructure’ — for example, water — ‘is funded by private sector capital through privately owned, independently regulated utilities, but roads in Britain still call on the public finances for funding?’ We need to look urgently at the options for getting large-scale private investment into the national roads network; from sovereign wealth funds, from pension funds, from other investors. That is why I’ve asked the Department for Transport and the Treasury to carry out a feasibility study of new ownership and financing models for the national roads system and to report progress to me in the autumn. Let me be clear: this is not about mass tolling and, as I’ve said, we’re not tolling existing roads; it’s about getting more out of the money that motorists already pay.