A select link analysis method based on logit–weibit hybrid model
A select link analysis method based on logit-weibit hybrid model
Pengjie Liu 0 1 2
Xiangdong Xu 0 1 2
Anthony Chen 0 1 2
Chao Yang 0 1 2
Longwen Xiao 0 1 2
0 School of Traffic and Transportation Engineering, Central South University , Changsha , China
1 Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University , Kowloon , Hong Kong
2 & Xiangdong Xu
Select link analysis provides information of where traffic comes from and goes to at selected links. This disaggregate information has wide applications in practice. The state-of-the-art planning software packages often adopt the user equilibrium (UE) model for select link analysis. However, empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model. This paper proposes an alternative select link analysis method by making use of the recently developed logit-weibit hybrid model, to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression. To enhance the applicability in large-scale networks, Bell's stochastic loading method originally developed for logit model is adapted to the hybrid model. The features of the proposed method are twofold: (1) unique O-D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and (2) applicability in large-scale networks due to the link-based stochastic loading method. An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O-D-specific link flow information. A visualization method is also proposed to enhance the understanding of O-D-specific link flow originally in the form of a matrix.
Select link analysis; Logit model model; Hybrid model; Bell loading; Weibit
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The Key Laboratory of Road and Traffic Engineering of the
Ministry of Education, Tongji University, Shanghai, China
1 Introduction
Select link analysis provides information of where traffic
comes from and goes to at selected links, i.e., the spatial
distribution and origin–destination (O–D) pair composition
of aggregate link flow. This disaggregate information has
wide applications in practice. For example, this
disaggregate information enables to quantify or predict the impact
of transportation planning, operations, control and
management schemes from multiple perspectives such as
congestion alleviation, environmental sustainability and
inequity. In detail, we need this information to examine the
spatial impacts of a proposed road improvement project.
Although the License Plate Recognition system can track
partial paths of vehicles to analyze link flow composition,
it is still unable to cover the whole transportation network.
More importantly, the traffic condition after implementing
a transportation planning or management scheme cannot be
observed beforehand. Moreover, the state-of-the-art
planning software packages often adopt the user equilibrium
(UE) model for select link analysis. It is well known that
the O–D-specific link flow in the UE model is non-unique,
hence requiring an additional criterion for selecting a
reasonable solution. To resolve this dilemma, Lu and Nie [1]
explored continuity of the UE link flow, proposed a general
resolution based on the maximum entropy UE and
decomposed the total flow of a link by O–D pairs to
examine the spatial impacts of a transportation project.
Later on, Bar-Gera et al. [2] proposed an additional
condition of proportionality for select link analysis. On the
other hand, empirical studies have repeatedly revealed that
the stochastic user equilibrium (SUE) model more
accurately predicts observed mean and variance of choices than
the UE model (e.g., [3]). Hence, we aim to use a more
accurate probabilistic route choice model to develop a new
select link analysis method with both behavioral realism
and large-scale network applicability.
In the literature, there are two main types of closed-form
probabilistic route choice models: logit [4] and weibit [5].
As to the logit model, Dial [4] developed a link-based
stochastic loading method with a forward pass and a
backward pass. Van Vliet [6] derived the link choice
probability expression according to link weights based on
Dial’s algorithm. Bell [7] proposed two logit assignment
methods as alternatives to Dial’s algorithm without path
enumeration. Akamatsu [8] utilized the decomposition of
path choice entropy to transform path-based into link-based
logit assignment. Owing to the homogeneity of variance in
the logit model, Nakayama [9] and Nakayama and
Chikaraishi [10] presented a q-generalization method based on
generalized extreme-value distribution to the random
component of utility to allow heteroscedastic variance.
Ahipas¸aog˘lu et al. [11] also introduced a marginal
distrib (...truncated)